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: "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.

Connected through hyperedge HE-1 (induced by source paper "ultimate intelligence part i physical completeness and objectivity of induction"):
  with members:
    - "an approximation of the universal intelligence measure"
    - "non equivalence of universal priors"
    - "models of quantum computation and quantum programming languages"
    - "some theorems on the algorithmic approach to probability theory and information theory"
    - "universal algorithmic intelligence a mathematical top down approach"
    - "optimal ordered problem solver"

Connected through hyperedge HE-2 (induced by source paper "a philosophical treatise of universal induction"):
  with members:
    - "a complete theory of everything will be subjective"
    - "a monte carlo aixi approximation"
    - "open problems in universal induction intelligence"
    - "algorithmic information theory a brief non technical guide to the field"
    - "algorithmic complexity bounds on future prediction errors"
    - "universal intelligence a definition of machine intelligence"
    - "similarity of objects and the meaning of words"
    - "adaptive online prediction by following the perturbed leader"

Connected through hyperedge HE-3 (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-4 (induced by source paper "universal algorithmic intelligence a mathematical top down approach"):
  with members:
    - "the fastest and shortest algorithm for all well defined problems"
    - "self optimizing and pareto optimal policies in general environments based on bayes mixtures"
    - "optimal ordered problem solver"
    - "universal sequential decisions in unknown environments"
    - "general loss bounds for universal sequence prediction"
    - "gradient based reinforcement planning in policy search methods"
    - "convergence and error bounds for universal prediction of nonbinary sequences"
    - "market based reinforcement learning in partially observable worlds"

Connected through hyperedge HE-5 (induced by source paper "algorithmic complexity bounds on future prediction errors"):
  with members:
    - "monotone conditional complexity bounds on future prediction errors"
    - "sequential predictions based on algorithmic complexity"
    - "convergence of discrete mdl for sequential prediction"
    - "universal convergence of semimeasures on individual random sequences"
    - "sequence prediction based on monotone complexity"
    - "optimality of universal bayesian sequence prediction for general loss and alphabet"
    - "general loss bounds for universal sequence prediction"
    - "convergence and error bounds for universal prediction of nonbinary sequences"

Connected through hyperedge HE-6 (induced by source paper "asymptotics of discrete mdl for online prediction"):
  with members:
    - "adaptive online prediction by following the perturbed leader"
    - "convergence of discrete mdl for sequential prediction"
    - "on the convergence speed of mdl predictions for bernoulli sequences"
    - "sequence prediction based on monotone complexity"
    - "the similarity metric"
    - "minimum description length induction bayesianism and kolmogorov complexity"
    - "new error bounds for solomonoff prediction"

Connected through hyperedge HE-7 (induced by source paper "sequential predictions based on algorithmic complexity"):
  with members:
    - "convergence of discrete mdl for sequential prediction"
    - "on the convergence speed of mdl predictions for bernoulli sequences"
    - "sequence prediction based on monotone complexity"
    - "convergence and error bounds for universal prediction of nonbinary sequences"
    - "minimum description length induction bayesianism and kolmogorov complexity"
    - "new error bounds for solomonoff prediction"

Connected through hyperedge HE-8 (induced by source paper "monotone conditional complexity bounds on future prediction errors"):
  with members:
    - "convergence of discrete mdl for sequential prediction"
    - "universal convergence of semimeasures on individual random sequences"
    - "sequence prediction based on monotone complexity"
    - "optimality of universal bayesian sequence prediction for general loss and alphabet"
    - "convergence and error bounds for universal prediction of nonbinary sequences"

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)