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: "partition tree weighting"
  Abstract: This paper introduces the Partition Tree Weighting technique, an efficient meta-algorithm for piecewise stationary sources. The technique works by performing Bayesian model averaging over a large class of possible partitions of the data into locally stationary segments. It uses a prior, closely related to the Context Tree Weighting technique of Willems, that is well suited to data compression applications. Our technique can be applied to any coding distribution at an additional time and space cost only logarithmic in the sequence length. We provide a competitive analysis of the redundancy of our method, and explore its application in a variety of settings. The order of the redundancy and the complexity of our algorithm matches those of the best competitors available in the literature, and the new algorithm exhibits a superior complexity-performance trade-off in our experiments.

Connected through hyperedge HE-1 (induced by source paper "generalized probability smoothing"):
  with members:
    - "soft bayes prod for mixtures of experts with log loss"
    - "on probability estimation by exponential smoothing"
    - "on probability estimation via relative frequencies and discount"
    - "context tree switching"
    - "combining non stationary prediction optimization and mixing for data compression"

Connected through hyperedge HE-2 (induced by source paper "on probability estimation by exponential smoothing"):
  with members:
    - "on probability estimation via relative frequencies and discount"
    - "adaptive context tree weighting"
    - "context tree switching"
    - "combining non stationary prediction optimization and mixing for data compression"

Connected through hyperedge HE-3 (induced by source paper "compress and control"):
  with members:
    - "the arcade learning environment an evaluation platform for general agents"
    - "sparse adaptive dirichlet multinomial like processes"
    - "playing atari with deep reinforcement learning"
    - "learning is planning near bayes optimal reinforcement learning via monte carlo tree search"
    - "efficient bayes adaptive reinforcement learning using sample based search"
    - "efficient tracking of large classes of experts"
    - "reinforcement learning via aixi approximation"
    - "a monte carlo aixi approximation"

Connected through hyperedge HE-4 (induced by source paper "online learning of k cnf boolean functions"):
  with members:
    - "linear and geometric mixtures analysis"
    - "context tree switching"

Connected through hyperedge HE-5 (induced by source paper "on probability estimation via relative frequencies and discount"):
  with members:
    - "context tree switching"
    - "combining non stationary prediction optimization and mixing for data compression"

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)