human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "mutual information relative entropy and estimation in the poisson channel"
  Abstract: Let $X$ be a non-negative random variable and let the conditional distribution of a random variable $Y$, given $X$, be ${Poisson}(\gamma \cdot X)$, for a parameter $\gamma \geq 0$. We identify a natural loss function such that: 1) The derivative of the mutual information between $X$ and $Y$ with respect to $\gamma$ is equal to the \emph{minimum} mean loss in estimating $X$ based on $Y$, regardless of the distribution of $X$. 2) When $X \sim P$ is estimated based on $Y$ by a mismatched estimator that would have minimized the expected loss had $X \sim Q$, the integral over all values of $\gamma$ of the excess mean loss is equal to the relative entropy between $P$ and $Q$. #R##N#For a continuous time setting where $X^T = \{X_t, 0 \leq t \leq T \}$ is a non-negative stochastic process and the conditional law of $Y^T=\{Y_t, 0\le t\le T\}$, given $X^T$, is that of a non-homogeneous Poisson process with intensity function $\gamma \cdot X^T$, under the same loss function: 1) The minimum mean loss in \emph{causal} filtering when $\gamma = \gamma_0$ is equal to the expected value of the minimum mean loss in \emph{non-causal} filtering (smoothing) achieved with a channel whose parameter $\gamma$ is uniformly distributed between 0 and $\gamma_0$. Bridging the two quantities is the mutual information between $X^T$ and $Y^T$. 2) This relationship between the mean losses in causal and non-causal filtering holds also in the case where the filters employed are mismatched, i.e., optimized assuming a law on $X^T$ which is not the true one. Bridging the two quantities in this case is the sum of the mutual information and the relative entropy between the true and the mismatched distribution of $Y^T$. Thus, relative entropy quantifies the excess estimation loss due to mismatch in this setting. #R##N#These results parallel those recently found for the Gaussian channel.

Connected through hyperedge HE-1 (induced by source paper "a class of lower bounds for bayesian risk with a bregman loss"):
  with members:
    - "estimation in poisson noise properties of the conditional mean estimator"
    - "mutual information relative entropy and estimation error in semi martingale channels"
    - "functional bregman divergence and bayesian estimation of distributions"
    - "mutual information and minimum mean square error in gaussian channels"

Connected through hyperedge HE-2 (induced by source paper "estimation in poisson noise properties of the conditional mean estimator"):
  with members:
    - "mutual information relative entropy and estimation error in semi martingale channels"
    - "a de bruijn identity for discrete random variables"
    - "strong data processing inequalities for input constrained additive noise channels"
    - "bounds on the lambert function and their application to the outage analysis of user cooperation"
    - "on regret of parametric mismatch in minimum mean square error estimation"
    - "estimation in gaussian noise properties of the minimum mean square error"
    - "compressed sensing performance bounds under poisson noise"
    - "mutual information and minimum mean square error in gaussian channels"

Connected through hyperedge HE-3 (induced by source paper "a generalized expression for the gradient of mutual information with the application in multiple access channels"):
  with members:
    - "sum rate capacity for symmetric gaussian multiple access channels with feedback"
    - "transmitter optimization in slow fading miso wiretap channel"
    - "generalized i mmse for k user gaussian channels"
    - "pointwise relations between information and estimation in gaussian noise"
    - "mutual information and minimum mean square error in gaussian channels"

Connected through hyperedge HE-4 (induced by source paper "capacity upper bounds for deletion type channels"):
  with members:
    - "sharp analytical capacity upper bounds for sticky and related channels"
    - "expressions for the entropy of binomial type distributions"
    - "edit distance sketching streaming and document exchange"
    - "coding against deletions in oblivious and online models"
    - "an improved bound on the fraction of correctable deletions"
    - "efficient low redundancy codes for correcting multiple deletions"
    - "deletion codes in the high noise and high rate regimes"
    - "list decoding subspace codes from insertions and deletions"

Connected through hyperedge HE-5 (induced by source paper "a survey on mimo transmission with discrete input signals technical challenges advances and future trends"):
  with members:
    - "asymptotic performance analysis of a k hop amplify and forward relay mimo channel"
    - "iterative distributed minimum total mse approach for secure communications in mimo interference channels"
    - "asymptotic analysis of su mimo channels with transmitter noise and mismatched joint decoding"
    - "secure massive mimo transmission with an active eavesdropper"
    - "interference aligned space time transmission with diversity for the 2 times 2 x network"
    - "space time encoded miso broadcast channel with outdated csit an error rate and diversity performance analysis"
    - "high snr asymptotics of mutual information for discrete constellations with applications to bicm"
    - "differential distributed space time coding with imperfect synchronization in frequency selective channels"

Connected through hyperedge HE-6 (induced by source paper "information and estimation in fokker planck channels"):
  with members:
    - "generalization of the de bruijn s identity to general phi entropies and phi fisher informations"
    - "higher order derivatives in costa s entropy power inequality"
    - "pointwise relations between information and estimation in gaussian noise"
    - "mutual information and minimum mean square error in gaussian channels"

Connected through hyperedge HE-7 (induced by source paper "mutual information relative entropy and estimation error in semi martingale channels"):
  with members:
    - "a de bruijn identity for symmetric stable laws"
    - "pointwise relations between information and estimation in gaussian noise"
    - "mutual information and minimum mean square error in gaussian channels"

Connected through hyperedge HE-8 (induced by source paper "expressions for the entropy of binomial type distributions"):
  with members:
    - "capacity upper bounds for deletion type channels"

Question: Please tell me which class the center node belongs to. The 40 classes are: cs.NA(Numerical Analysis), cs.MM(Multimedia), cs.LO(Logic in Computer Science), cs.CY(Computers and Society), cs.CR(Cryptography and Security), cs.DC(Distributed, Parallel, and Cluster Computing), cs.HC(Human-Computer Interaction), cs.CE(Computational Engineering, Finance, and Science), cs.NI(Networking and Internet Architecture), cs.CC(Computational Complexity), cs.AI(Artificial Intelligence), cs.MA(Multiagent Systems), cs.GL(General Literature), cs.NE(Neural and Evolutionary Computing), cs.SC(Symbolic Computation), cs.AR(Hardware Architecture), cs.CV(Computer Vision and Pattern Recognition), cs.GR(Graphics), cs.ET(Emerging Technologies), cs.SY(Systems and Control), cs.CG(Computational Geometry), cs.OH(Other Computer Science), cs.PL(Programming Languages), cs.SE(Software Engineering), cs.LG(Machine Learning), cs.SD(Sound), cs.SI(Social and Information Networks), cs.RO(Robotics), cs.IT(Information Theory), cs.PF(Performance), cs.CL(Computation and Language), cs.IR(Information Retrieval), cs.MS(Mathematical Software), cs.FL(Formal Languages and Automata Theory), cs.DS(Data Structures and Algorithms), cs.OS(Operating Systems), cs.GT(Computer Science and Game Theory), cs.DB(Databases), cs.DL(Digital Libraries), cs.DM(Discrete Mathematics). Directly output the class name.

gpt: cs.IT(Information Theory)