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 "unsupervised model free representation learning"):
  Abstract: Numerous control and learning problems face the situation where sequences of high-dimensional highly dependent data are available, but no or little feedback is provided to the learner. To address this issue, we formulate the following problem. Given a series of observations X_0,...,X_n coming from a large (high-dimensional) space X, find a representation function f mapping X to a finite space Y such that the series f(X_0),...,f(X_n) preserve as much information as possible about the original time-series dependence in X_0,...,X_n. We show that, for stationary time series, the function f can be selected as the one maximizing the time-series information h_0(f(X))- h_\infty (f(X)) where h_0(f(X)) is the Shannon entropy of f(X_0) and h_\infty (f(X)) is the entropy rate of the time series f(X_0),...,f(X_n),... Implications for the problem of optimal control are presented.

Includes members:
  Member "optimal regret bounds for selecting the state representation in reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "near optimal exploration exploitation in non communicating markov decision processes")
  Member "selecting the state representation in reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning representations in model free hierarchical reinforcement learning")
  Member "methods for computing state similarity in markov decision processes":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning discrete state abstractions with deep variational inference")
  Member "consistency of feature markov processes":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "extreme state aggregation beyond mdps")
  Member "feature reinforcement learning part i unstructured mdps":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a geometric perspective on optimal representations for reinforcement learning")
  Member "universal codes as a basis for time series testing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "independence clustering without a matrix")
  Member "pattern recognition for conditionally independent data":
    also connected through other hyperedges:
      (none)
  Member "using information theory approach to randomness testing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "parallel decompression of gzip compressed files and random access to dna sequences")

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.LG(Machine Learning)