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: "non equivalence of universal priors"
  Abstract: Ray Solomonoff invented the notion of universal induction featuring an aptly termed "universal" prior probability function over all possible computable environments. The essential property of this prior was its ability to dominate all other such priors. Later, Levin introduced another construction --- a mixture of all possible priors or `universal mixture'. These priors are well known to be equivalent up to multiplicative constants. Here, we seek to clarify further the relationships between these three characterisations of a universal prior (Solomonoff's, universal mixtures, and universally dominant priors). We see that the the constructions of Solomonoff and Levin define an identical class of priors, while the class of universally dominant priors is strictly larger. We provide some characterisation of the discrepancy.

Connected through hyperedge HE-1 (induced by source paper "loss bounds and time complexity for speed priors"):
  with members:
    - "on the computability of solomonoff induction and knowledge seeking"
    - "a monte carlo aixi approximation"
    - "optimal ordered problem solver"

Connected through hyperedge HE-2 (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"
    - "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"
    - "a theory of universal artificial intelligence based on algorithmic complexity"

Connected through hyperedge HE-3 (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"
    - "universal prediction of selected bits"
    - "a philosophical treatise of universal induction"
    - "a theory of universal artificial intelligence based on algorithmic complexity"
    - "new error bounds for solomonoff prediction"

Connected through hyperedge HE-4 (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"
    - "a philosophical treatise of universal induction"
    - "a monte carlo aixi approximation"
    - "a theory of universal artificial intelligence based on algorithmic complexity"
    - "new error bounds for solomonoff prediction"

Connected through hyperedge HE-5 (induced by source paper "ultimate intelligence part i physical completeness and objectivity of induction"):
  with members:
    - "an approximation of the universal intelligence measure"
    - "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"
    - "convergence and loss bounds for bayesian sequence prediction"
    - "optimal ordered problem solver"

Connected through hyperedge HE-6 (induced by source paper "solomonoff induction violates nicod s criterion"):
  with members:
    - "a philosophical treatise of universal induction"
    - "new error bounds for solomonoff prediction"

Connected through hyperedge HE-7 (induced by source paper "one decade of universal artificial intelligence"):
  with members:
    - "can intelligence explode"
    - "principles of solomonoff induction and aixi"
    - "asymptotically optimal agents"
    - "universal learning theory"
    - "an approximation of the universal intelligence measure"
    - "axioms for rational reinforcement learning"
    - "feature reinforcement learning in practice"
    - "a philosophical treatise of universal induction"

Connected through hyperedge HE-8 (induced by source paper "principles of solomonoff induction and aixi"):
  with members:
    - "axioms for rational reinforcement learning"
    - "a philosophical treatise of universal induction"

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.IT(Information Theory)