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: "on an improvement over renyi s equivocation bound"
  Abstract: We consider the problem of estimating the probability of error in multi-hypothesis testing when MAP criterion is used. This probability, which is also known as the Bayes risk is an important measure in many communication and information theory problems. In general, the exact Bayes risk can be difficult to obtain. Many upper and lower bounds are known in literature. One such upper bound is the equivocation bound due to R\'enyi which is of great philosophical interest because it connects the Bayes risk to conditional entropy. Here we give a simple derivation for an improved equivocation bound. #R##N#We then give some typical examples of problems where these bounds can be of use. We first consider a binary hypothesis testing problem for which the exact Bayes risk is difficult to derive. In such problems bounds are of interest. Furthermore using the bounds on Bayes risk derived in the paper and a random coding argument, we prove a lower bound on equivocation valid for most random codes over memoryless channels.

Connected through hyperedge HE-1 (induced by source paper "generating optimal privacy protection mechanisms via machine learning"):
  with members:
    - "privacy preserving adversarial networks"
    - "back to the drawing board revisiting the design of optimal location privacy preserving mechanisms"
    - "differential privacy as a mutual information constraint"
    - "on large batch training for deep learning generalization gap and sharp minima"
    - "privacy games optimal user centric data obfuscation"
    - "optimal geo indistinguishable mechanisms for location privacy"
    - "geo indistinguishability differential privacy for location based systems"
    - "compositional closure for bayes risk in probabilistic noninterference"

Connected through hyperedge HE-2 (induced by source paper "f bleau fast black box leakage estimation"):
  with members:
    - "mine mutual information neural estimation"
    - "learning to explain an information theoretic perspective on model interpretation"
    - "bayes not naive security bounds on website fingerprinting defenses"
    - "back to the drawing board revisiting the design of optimal location privacy preserving mechanisms"
    - "membership inference attacks against machine learning models"
    - "infogan interpretable representation learning by information maximizing generative adversarial nets"
    - "geo indistinguishability differential privacy for location based systems"

Connected through hyperedge HE-3 (induced by source paper "formal approaches to information hiding an analysis of interactive systems statistical disclosure control and refinement of specifications"):
  with members:
    - "the capacity of channels with feedback"

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)