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 "convergence and loss bounds for bayesian sequence prediction"):
  Abstract: The probability of observing x/sub t/ at time t, given past observations x/sub 1/...x/sub t-1/ can be computed if the true generating distribution /spl mu/ of the sequences x/sub 1/x/sub 2/x/sub 3/... is known. If /spl mu/ is unknown, but known to belong to a class /spl Mscr/ one can base one's prediction on the Bayes mix /spl xi/ defined as a weighted sum of distributions /spl nu/ /spl isin/ /spl Mscr/. Various convergence results of the mixture posterior /spl xi//sub t/ to the true posterior /spl mu//sub t/ are presented. In particular, a new (elementary) derivation of the convergence /spl xi//sub t///spl mu//sub t/ /spl rarr/ 1 is provided, which additionally gives the rate of convergence. A general sequence predictor is allowed to choose an action y/sub t/ based on x/sub 1/...x/sub t-1/ and receives loss /spl lscr//sub x(t)y(t)/ if x/sub t/ is the next symbol of the sequence. No assumptions are made on the structure of /spl lscr/ (apart from being bounded) and /spl Mscr/. The Bayes-optimal prediction scheme /spl Lambda//sub /spl xi// based on mixture /spl xi/ and the Bayes-optimal informed prediction scheme /spl Lambda//sub /spl mu// are defined and the total loss L/sub /spl xi// of /spl Lambda//sub /spl xi// is bounded in terms of the total loss L/sub /spl mu// of /spl Lambda//sub /spl mu//. It is shown that L/sub /spl xi// is bounded for bounded L/sub /spl mu// and L/sub /spl xi///L/sub /spl mu// /spl rarr/ 1 for L/sub /spl mu// /spl rarr/ /spl infin/. Convergence of the instantaneous losses is also proven.

Includes members:
  Member "self optimizing and pareto optimal policies in general environments based on bayes mixtures":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "probabilistically safe policy transfer")
  Member "general loss bounds for universal sequence prediction":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "open problems in universal induction intelligence")
  Member "convergence and error bounds for universal prediction of nonbinary sequences":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "concentration and confidence for discrete bayesian sequence predictors")
  Member "minimum description length induction bayesianism and kolmogorov complexity":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "recursia rrt recursive translatable point set pattern discovery with removal of redundant translators")
  Member "new error bounds for solomonoff prediction":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "bad universal priors and notions of optimality")

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)