  In many sensor network applications, a fusion center often has additional
valuable information, such as context data, which cannot be obtained directly
from the sensors. Motivated by this, we study a generalized CEO problem where a
CEO has access to context information. The main contribution of this work is
twofold. Firstly, we characterize the asymptotically optimal error exponent per
rate as the number of sensors and sum rate grow without bound. The proof
extends the Berger-Tung coding scheme and the converse argument by Berger et
al. (1996) taking into account context information. The resulting expression
includes the minimum Chernoff divergence over context information. Secondly,
assuming that the sizes of the source and context alphabets are respectively
$|\mathcal{X}|$ and $|\mathcal{S}|$, we prove that it is asymptotically optimal
to partition all sensors into at most $\binom{|\mathcal{X}|}{2} |\mathcal{S}|$
groups and have the sensors in each group adopt the same encoding scheme. Our
problem subsumes the original CEO problem by Berger et al. (1996) as a special
case if there is only one letter for context information; in this case, our
result tightens its required number of groups from $\binom{|\mathcal{X}|}{2}+2$
to $\binom{|\mathcal{X}|}{2}$. We also numerically demonstrate the effect of
context information for a simple Gaussian scenario.
