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: "new error bounds for solomonoff prediction"
  Abstract: Solomonoff sequence prediction is a scheme to predict digits of binary strings without knowing the underlying probability distribution. We call a prediction scheme informed when it knows the true probability distribution of the sequence. Several new relations between universal Solomonoff sequence prediction and informed prediction and general probabilistic prediction schemes will be proved. Among others, they show that the number of errors in Solomonoff prediction is finite for computable distributions, if finite in the informed case. Deterministic variants will also be studied. The most interesting result is that the deterministic variant of Solomonoff prediction is optimal compared to any other probabilistic or deterministic prediction scheme apart from additive square root corrections only. This makes it well suited even for difficult prediction problems, where it does not suffice when the number of errors is minimal to within some factor greater than one. Solomonoff's original bound and the ones presented here complement each other in a useful way.

Connected through hyperedge HE-1 (induced by source paper "bad universal priors and notions of optimality"):
  with members:
    - "on the computability of aixi"
    - "optimistic agents are asymptotically optimal"
    - "asymptotically optimal agents"
    - "an approximation of the universal intelligence measure"
    - "non equivalence of universal priors"
    - "open problems in universal induction intelligence"
    - "universal intelligence a definition of machine intelligence"
    - "self optimizing and pareto optimal policies in general environments based on bayes mixtures"

Connected through hyperedge HE-2 (induced by source paper "on the computability of solomonoff induction and knowledge seeking"):
  with members:
    - "bad universal priors and notions of optimality"
    - "on the computability of aixi"
    - "asymptotically optimal agents"
    - "non equivalence of universal priors"
    - "universal prediction of selected bits"
    - "a philosophical treatise of universal induction"
    - "a theory of universal artificial intelligence based on algorithmic complexity"

Connected through hyperedge HE-3 (induced by source paper "on the computability of aixi"):
  with members:
    - "bad universal priors and notions of optimality"
    - "on the computability of solomonoff induction and knowledge seeking"
    - "non equivalence of universal priors"
    - "a philosophical treatise of universal induction"
    - "a monte carlo aixi approximation"
    - "a theory of universal artificial intelligence based on algorithmic complexity"

Connected through hyperedge HE-4 (induced by source paper "solomonoff induction violates nicod s criterion"):
  with members:
    - "non equivalence of universal priors"
    - "a philosophical treatise of universal induction"

Connected through hyperedge HE-5 (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-6 (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"
    - "convergence and error bounds for universal prediction of nonbinary sequences"

Connected through hyperedge HE-7 (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-8 (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"
    - "convergence and loss bounds for bayesian sequence prediction"
    - "the similarity metric"
    - "minimum description length induction bayesianism and kolmogorov complexity"

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.AI(Artificial Intelligence)