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: "functional bregman divergence and bayesian estimation of distributions"
  Abstract: A class of distortions termed functional Bregman divergences is defined, which includes squared error and relative entropy. A functional Bregman divergence acts on functions or distributions, and generalizes the standard Bregman divergence for vectors and a previous pointwise Bregman divergence that was defined for functions. A recently published result showed that the mean minimizes the expected Bregman divergence. The new functional definition enables the extension of this result to the continuous case to show that the mean minimizes the expected functional Bregman divergence over a set of functions or distributions. It is shown how this theorem applies to the Bayesian estimation of distributions. Estimation of the uniform distribution from independent and identically drawn samples is used as a case study.

Connected through hyperedge HE-1 (induced by source paper "reasoning about generalization via conditional mutual information"):
  with members:
    - "robust generalization via α mutual information"
    - "in defense of uniform convergence generalization via derandomization with an application to interpolating predictors"
    - "strengthened information theoretic bounds on the generalization error"
    - "tightening mutual information based bounds on generalization error"
    - "high probability generalization bounds for uniformly stable algorithms with nearly optimal rate"
    - "a new analysis of differential privacy s generalization guarantees"
    - "pac learning with stable and private predictions"
    - "a direct sum result for the information complexity of learning"

Connected through hyperedge HE-2 (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"
    - "mutual information relative entropy and estimation in the poisson channel"
    - "mutual information and minimum mean square error in gaussian channels"

Connected through hyperedge HE-3 (induced by source paper "copula variational bayes inference via information geometry"):
  with members:
    - "randomized dimensionality reduction for k means clustering"

Connected through hyperedge HE-4 (induced by source paper "iterative receivers for downlink mimo scma message passing and distributed cooperative detection"):
  with members:
    - "wireless powered communications with non orthogonal multiple access"
    - "nonorthogonal multiple access in large scale underlay cognitive radio networks"
    - "a survey of downlink non orthogonal multiple access for 5g wireless communication networks"
    - "gaussian message passing iterative detection for mimo noma systems with massive access"
    - "fast admm algorithm for distributed optimization with adaptive penalty"
    - "scma codebook design"
    - "what will 5g be"
    - "on the performance of non orthogonal multiple access in 5g systems with randomly deployed users"

Connected through hyperedge HE-5 (induced by source paper "calibrating noise to variance in adaptive data analysis"):
  with members:
    - "generalization for adaptively chosen estimators via stable median"
    - "information theoretic analysis of generalization capability of learning algorithms"
    - "learners that use little information"
    - "generalization in adaptive data analysis and holdout reuse"
    - "algorithmic stability for adaptive data analysis"
    - "preventing false discovery in interactive data analysis is hard"
    - "preserving statistical validity in adaptive data analysis"
    - "renyi divergence and kullback leibler divergence"

Connected through hyperedge HE-6 (induced by source paper "generalized bregman divergence and gradient of mutual information for vector poisson channels"):
  with members:
    - "communications inspired projection design with application to compressive sensing"
    - "information and estimation over binomial and negative binomial models"
    - "mutual information relative entropy and estimation in the poisson channel"
    - "mutual information and minimum mean square error in gaussian channels"

Connected through hyperedge HE-7 (induced by source paper "bregman divergence as general framework to estimate unnormalized statistical models"):
  with members:
    - "interpretation and generalization of score matching"
    - "a family of computationally efficient and simple estimators for unnormalized statistical models"

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)