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 f divergences integral representations local behavior and inequalities"
  Abstract: This paper is focused on f-divergences, consisting of three main contributions. The first one introduces integral representations of a general f-divergence by means of the relative information spectrum. The second part provides a new approach for the derivation of f-divergence inequalities, and it exemplifies their utility in the setup of Bayesian binary hypothesis testing. The last part of this paper further studies the local behavior of f-divergences.

Connected through hyperedge HE-1 (induced by source paper "on data processing and majorization inequalities for f divergences with applications"):
  with members:
    - "minimum entropy couplings and their applications"
    - "comparison of channels criteria for domination by a symmetric channel"
    - "strong data processing inequalities for channels and bayesian networks"
    - "bounds on the entropy of a function of a random variable and their applications"
    - "f divergence inequalities"
    - "e_ gamma resolvability"
    - "bounds between contraction coefficients"
    - "strong data processing inequalities for input constrained additive noise channels"

Connected through hyperedge HE-2 (induced by source paper "on a generalization of the jensen shannon divergence"):
  with members:
    - "on the jensen shannon symmetrization of distances relying on abstract means"
    - "monte carlo information geometry the dually flat case"
    - "information measures the curious case of the binary alphabet"
    - "jeffreys centroids a closed form expression for positive histograms and a guaranteed tight approximation for frequency histograms"
    - "the burbea rao and bhattacharyya centroids"
    - "a family of statistical symmetric divergences based on jensen s inequality"
    - "measures of distributional similarity"

Connected through hyperedge HE-3 (induced by source paper "bregman divergence bounds and universality properties of the logarithmic loss"):
  with members:
    - "on the universality of the logistic loss function"
    - "universality of logarithmic loss in lossy compression"
    - "opening the black box of deep neural networks via information"
    - "f divergence inequalities"
    - "generalized independent component analysis over finite alphabets"
    - "justification of logarithmic loss via the benefit of side information"
    - "deep learning and the information bottleneck principle"
    - "information measures the curious case of the binary alphabet"

Connected through hyperedge HE-4 (induced by source paper "variational inference with tail adaptive f divergence"):
  with members:
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "f divergence constrained policy improvement"
    - "the concrete distribution a continuous relaxation of discrete random variables"
    - "importance weighted autoencoders"
    - "adam a method for stochastic optimization"
    - "expectation propagation for approximate bayesian inference"

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)