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 error bounds for universal prediction of nonbinary sequences"
  Abstract: Solomonoff's uncomputable universal prediction scheme $\xi$ allows to predict the next symbol $x_k$ of a sequence $x_1...x_{k-1}$ for any Turing computable, but otherwise unknown, probabilistic environment $\mu$. This scheme will be generalized to arbitrary environmental classes, which, among others, allows the construction of computable universal prediction schemes $\xi$. Convergence of $\xi$ to $\mu$ in a conditional mean squared sense and with $\mu$ probability 1 is proven. It is shown that the average number of prediction errors made by the universal $\xi$ scheme rapidly converges to those made by the best possible informed $\mu$ scheme. The schemes, theorems and proofs are given for general finite alphabet, which results in additional complications as compared to the binary case. Several extensions of the presented theory and results are outlined. They include general loss functions and bounds, games of chance, infinite alphabet, partial and delayed prediction, classification, and more active systems.

Connected through hyperedge HE-1 (induced by source paper "concentration and confidence for discrete bayesian sequence predictors"):
  with members:
    - "optimality of universal bayesian sequence prediction for general loss and alphabet"

Connected through hyperedge HE-2 (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-3 (induced by source paper "universal algorithmic intelligence a mathematical top down approach"):
  with members:
    - "convergence and loss bounds for bayesian sequence prediction"
    - "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"
    - "market based reinforcement learning in partially observable worlds"

Connected through hyperedge HE-4 (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"
    - "convergence and loss bounds for bayesian sequence prediction"
    - "optimality of universal bayesian sequence prediction for general loss and alphabet"
    - "general loss bounds for universal sequence prediction"

Connected through hyperedge HE-5 (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 loss bounds for bayesian sequence prediction"
    - "minimum description length induction bayesianism and kolmogorov complexity"
    - "new error bounds for solomonoff prediction"

Connected through hyperedge HE-6 (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"
    - "convergence and loss bounds for bayesian sequence prediction"
    - "optimality of universal bayesian sequence prediction for general loss and alphabet"

Connected through hyperedge HE-7 (induced by source paper "on generalized computable universal priors and their convergence"):
  with members:
    - "universal convergence of semimeasures on individual random sequences"
    - "sequence prediction based on monotone complexity"
    - "minimum description length induction bayesianism and kolmogorov complexity"

Connected through hyperedge HE-8 (induced by source paper "convergence of discrete mdl for sequential prediction"):
  with members:
    - "on the convergence speed of mdl predictions for bernoulli sequences"
    - "sequence prediction based on monotone complexity"
    - "optimality of universal bayesian sequence prediction for general loss and alphabet"
    - "the similarity metric"
    - "new error bounds for solomonoff prediction"

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)