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 sequence prediction for arbitrary measures"
  Abstract: Suppose we are given two probability measures on the set of one-way infinite finite-alphabet sequences and consider the question when one of the measures predicts the other, that is, when conditional probabilities converge (in a certain sense) when one of the measures is chosen to generate the sequence. This question may be considered a refinement of the problem of sequence prediction in its most general formulation: for a given class of probability measures, does there exist a measure which predicts all of the measures in the class? To address this problem, we find some conditions on local absolute continuity which are sufficient for prediction and which generalize several different notions which are known to be sufficient for prediction. We also formulate some open questions to outline a direction for finding the conditions on classes of measures for which prediction is possible.

Connected through hyperedge HE-1 (induced by source paper "offline to online conversion"):
  with members:
    - "indefinitely oscillating martingales"
    - "asymptotics of discrete mdl for online prediction"
    - "optimality of universal bayesian sequence prediction for general loss and alphabet"

Connected through hyperedge HE-2 (induced by source paper "on the relation between realizable and nonrealizable cases of the sequence prediction problem"):
  with members:
    - "sequence prediction in realizable and non realizable cases"
    - "characterizing predictable classes of processes"
    - "on finding predictors for arbitrary families of processes"

Connected through hyperedge HE-3 (induced by source paper "sequence prediction in realizable and non realizable cases"):
  with members:
    - "characterizing predictable classes of processes"
    - "on finding predictors for arbitrary families of processes"

Connected through hyperedge HE-4 (induced by source paper "open problems in universal induction intelligence"):
  with members:
    - "policy iteration for factored mdps"
    - "feature dynamic bayesian networks"
    - "online planning algorithms for pomdps"
    - "feature markov decision processes"
    - "on the possibility of learning in reactive environments with arbitrary dependence"
    - "a collection of definitions of intelligence"
    - "universal algorithmic intelligence a mathematical top down approach"
    - "tests of machine intelligence"

Connected through hyperedge HE-5 (induced by source paper "on finding predictors for arbitrary families of processes"):
  with members:
    - "characterizing predictable classes of processes"
    - "on the possibility of learning in reactive environments with arbitrary dependence"
    - "universal codes as a basis for time series testing"

Connected through hyperedge HE-6 (induced by source paper "on the possibility of learning in reactive environments with arbitrary dependence"):
  with members:
    - "general discounting versus average reward"
    - "defensive universal learning with experts"
    - "universal learning of repeated matrix games"
    - "prediction with expert advice by following the perturbed leader for general weights"
    - "optimality of universal bayesian sequence prediction for general loss and alphabet"
    - "self optimizing and pareto optimal policies in general environments based on bayes mixtures"

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.LG(Machine Learning)