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: "tight bounds for symmetric divergence measures and a new inequality relating f divergences"
  Abstract: Tight bounds for several symmetric divergence measures are introduced, given in terms of the total variation distance. Each of these bounds is attained by a pair of 2 or 3-element probability distributions. An application of these bounds for lossless source coding is provided, refining and improving a certain bound by Csiszar. A new inequality relating f-divergences is derived, and its use is exemplified. The last section of this conference paper is not included in the recent journal paper [16], as well as some new remarks that are linked to new references.

Connected through hyperedge HE-1 (induced by source paper "chernoff information between gaussian trees"):
  with members:
    - "chernoff information of bottleneck gaussian trees"
    - "on model misspecification and kl separation for gaussian graphical models"
    - "representation learning a review and new perspectives"
    - "information theoretic limits of selecting binary graphical models in high dimensions"

Connected through hyperedge HE-2 (induced by source paper "bounds between contraction coefficients"):
  with members:
    - "comparison of channels criteria for domination by a symmetric channel"
    - "f divergence inequalities"
    - "strong data processing inequalities for input constrained additive noise channels"
    - "information extraction under privacy constraints"
    - "dissipation of information in channels with input constraints"
    - "strong data processing inequalities and phi sobolev inequalities for discrete channels"
    - "linear information coupling problems"
    - "on maximal correlation hypercontractivity and the data processing inequality studied by erkip and cover"

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)