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 "tight bounds for symmetric divergence measures and a refined bound for lossless source coding"):
  Abstract: Tight bounds for several symmetric divergence measures are derived in terms of the total variation distance. It is shown that each of these bounds is attained by a pair of two- or three-element probability distributions. An application of these bounds for lossless source coding is provided, refining and improving a certain bound by Csiszar. Another application of these bounds has been recently introduced by Yardi  et al.  for channel-code detection.

Includes members:
  Member "renyi divergence and kullback leibler divergence":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "review analyze and design a comprehensive deep reinforcement learning framework")
  Member "improved lower bounds on the total variation distance for the poisson approximation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "entropy bounds for discrete random variables via maximal coupling")
  Member "entropy bounds for discrete random variables via maximal coupling":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "minimum entropy couplings and their applications")
  Member "an information theoretic perspective of the poisson approximation via the chen stein method":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "entropy bounds for discrete random variables via maximal coupling")
  Member "on pinsker s and vajda s type inequalities for csiszar s f divergences":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "theoretical evidence for adversarial robustness through randomization")
  Member "on pairs of f divergences and their joint range":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a better bound gives a hundred rounds enhanced privacy guarantees via f divergences")

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)