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 reverse pinsker inequalities"
  Abstract: New upper bounds on the relative entropy are derived as a function of the total variation distance. One bound refines an inequality by Verd\'{u} for general probability measures. A second bound improves the tightness of an inequality by Csisz\'{a}r and Talata for arbitrary probability measures that are defined on a common finite set. The latter result is further extended, for probability measures on a finite set, leading to an upper bound on the R\'{e}nyi divergence of an arbitrary non-negative order (including $\infty$) as a function of the total variation distance. Another lower bound by Verd\'{u} on the total variation distance, expressed in terms of the distribution of the relative information, is tightened and it is attained under some conditions. The effect of these improvements is exemplified.

Connected through hyperedge HE-1 (induced by source paper "sample efficient model free reinforcement learning with off policy critics"):
  with members:
    - "exploration by random network distillation"
    - "dual policy iteration"
    - "information directed exploration for deep reinforcement learning"
    - "addressing function approximation error in actor critic methods"
    - "distributed prioritized experience replay"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "rudder return decomposition for delayed rewards"
    - "rainbow combining improvements in deep reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "a note on reverse pinsker inequalities"):
  with members:
    - "f divergence inequalities"
    - "optimal quantization for distribution synthesis"

Connected through hyperedge HE-3 (induced by source paper "guaranteed deterministic bounds on the total variation distance between univariate mixtures"):
  with members:
    - "guaranteed bounds on information theoretic measures of univariate mixtures using piecewise log sum exp inequalities"
    - "f divergence inequalities"
    - "on the chi square and higher order chi distances for approximating f divergences"
    - "generalized bhattacharyya and chernoff upper bounds on bayes error using quasi arithmetic means"
    - "generalised pinsker inequalities"
    - "statistical exponential families a digest with flash cards"

Connected through hyperedge HE-4 (induced by source paper "existence and continuity of differential entropy for a class of distributions"):
  with members:
    - "wasserstein continuity of entropy and outer bounds for interference channels"
    - "on convergence properties of shannon entropy"

Connected through hyperedge HE-5 (induced by source paper "clustering via crowdsourcing"):
  with members:
    - "parallel algorithms for select and partition with noisy comparisons"
    - "fault tolerant entity resolution with the crowd"
    - "stochastic block model and community detection in the sparse graphs a spectral algorithm with optimal rate of recovery"
    - "parallel correlation clustering on big graphs"
    - "exact recovery in the stochastic block model"
    - "crowder crowdsourcing entity resolution"
    - "sorting searching and simulation in the mapreduce framework"
    - "fast clustering using mapreduce"

Connected through hyperedge HE-6 (induced by source paper "optimal quantization for distribution synthesis"):
  with members:
    - "greedy algorithms for optimal distribution approximation"
    - "f divergence inequalities"
    - "renyi divergence and kullback leibler divergence"
    - "fixed to variable length resolution coding for target distributions"
    - "informational divergence approximations to product distributions"
    - "optimal non uniform mapping for probabilistic shaping"
    - "matching dyadic distributions to channels"

Connected through hyperedge HE-7 (induced by source paper "comparing entropy rates on finite and infinite rooted trees with length functions"):
  with members:
    - "f divergence inequalities"
    - "informational divergence and entropy rate on rooted trees with probabilities"

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)