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 "reduction of markov chains using a value of information based approach"):
  Abstract: In this paper, we propose an approach to obtain reduced-order models of Markov chains. Our approach is composed of two information-theoretic processes. The first is a means of comparing pairs of stationary chains on different state spaces, which is done via the negative, modified Kullback–Leibler divergence defined on a model joint space. Model reduction is achieved by solving a value-of-information criterion with respect to this divergence. Optimizing the criterion leads to a probabilistic partitioning of the states in the high-order Markov chain. A single free parameter that emerges through the optimization process dictates both the partition uncertainty and the number of state groups. We provide a data-driven means of choosing the ‘optimal’ value of this free parameter, which sidesteps needing to a priori know the number of state groups in an arbitrary chain.

Includes members:
  Member "analysis of agent expertise in ms pac man using value of information based policies":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "guided policy exploration for markov decision processes using an uncertainty based value of information criterion")
  Member "guided policy exploration for markov decision processes using an uncertainty based value of information criterion":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "analysis of agent expertise in ms pac man using value of information based policies")
  Member "an analysis of the value of information when exploring stochastic discrete multi armed bandits":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "analysis of agent expertise in ms pac man using value of information based policies")
  Member "optimal kullback leibler aggregation via information bottleneck":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "co clustering via information theoretic markov aggregation")
  Member "a metric between probability distributions on finite sets of different cardinalities and applications to order reduction":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "minimum entropy couplings and their applications")

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)