human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "bounds between contraction coefficients"):
  Abstract: In this paper, we delineate how the contraction coefficient of the strong data processing inequality for KL divergence can be used to learn likelihood models. We then present an alternative formulation to learn likelihood models that forces the input KL divergence of the data processing inequality to vanish, and achieves a contraction coefficient equivalent to the squared maximal correlation. This formulation turns out to admit a linear algebraic solution. To analyze the performance loss in using this simple but suboptimal procedure, we bound these contraction coefficients in the discrete and finite regime, and prove their equivalence in the Gaussian regime.

Includes members:
  Member "comparison of channels criteria for domination by a symmetric channel":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a novel approach to multivariate redundancy and synergy")
  Member "f divergence inequalities":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on implicit regularization in beta vaes")
  Member "strong data processing inequalities for input constrained additive noise channels":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on data processing and majorization inequalities for f divergences with applications")
  Member "information extraction under privacy constraints":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on the information bottleneck problems models connections applications and information theoretic views")
  Member "tight bounds for symmetric divergence measures and a new inequality relating f divergences":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "chernoff information between gaussian trees")
  Member "dissipation of information in channels with input constraints":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "broadcasting on random directed acyclic graphs")
  Member "strong data processing inequalities and phi sobolev inequalities for discrete channels":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "on data processing and majorization inequalities for f divergences with applications")
  Member "linear information coupling problems":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "importance of small probability events in big data information measures applications and challenges")

Question: Please tell me which class the source paper 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)