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: "the new ai general sound relevant for physics"
  Abstract: Most traditional artificial intelligence (AI) systems of the past 50 years are either very limited, or based on heuristics, or both. The new millennium, however, has brought substantial progress in the field of theoretically optimal and practically feasible algorithms for prediction, search, inductive inference based on Occam's razor, problem solving, decision making, and reinforcement learning in environments of a very general type. Since inductive inference is at the heart of all inductive sciences, some of the results are relevant not only for AI and computer science but also for physics, provoking nontraditional predictions based on Zuse's thesis of the computer-generated universe.

Connected through hyperedge HE-1 (induced by source paper "universal induction with varying sets of combinators"):
  with members:
    - "binary space partitioning as intrinsic reward"
    - "cognitive bias for universal algorithmic intelligence"

Connected through hyperedge HE-2 (induced by source paper "cognitive bias for universal algorithmic intelligence"):
  with members:
    - "the fastest and shortest algorithm for all well defined problems"

Connected through hyperedge HE-3 (induced by source paper "driven by compression progress a simple principle explains essential aspects of subjective beauty novelty surprise interestingness attention curiosity creativity art science music jokes"):
  with members:
    - "2006 celebrating 75 years of ai history and outlook the next 25 years"
    - "simple algorithmic principles of discovery subjective beauty selective attention curiosity creativity"
    - "new millennium ai and the convergence of history"
    - "goedel machines self referential universal problem solvers making provably optimal self improvements"
    - "optimal ordered problem solver"
    - "reinforcement learning a survey"

Connected through hyperedge HE-4 (induced by source paper "simple algorithmic principles of discovery subjective beauty selective attention curiosity creativity"):
  with members:
    - "2006 celebrating 75 years of ai history and outlook the next 25 years"
    - "goedel machines self referential universal problem solvers making provably optimal self improvements"
    - "optimal ordered problem solver"
    - "reinforcement learning a survey"

Connected through hyperedge HE-5 (induced by source paper "new millennium ai and the convergence of history"):
  with members:
    - "evolino for recurrent support vector machines"
    - "goedel machines self referential universal problem solvers making provably optimal self improvements"
    - "the fastest and shortest algorithm for all well defined problems"
    - "reinforcement learning a survey"
    - "the neural network pushdown automation model stack and learning simulations"

Connected through hyperedge HE-6 (induced by source paper "goedel machines self referential universal problem solvers making provably optimal self improvements"):
  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"
    - "reinforcement learning a survey"

Connected through hyperedge HE-7 (induced by source paper "optimal ordered problem solver"):
  with members:
    - "goedel machines self referential universal problem solvers making provably optimal self improvements"
    - "the fastest and shortest algorithm for all well defined problems"
    - "self optimizing and pareto optimal policies in general environments based on bayes mixtures"
    - "market based reinforcement learning in partially observable worlds"
    - "towards a universal theory of artificial intelligence based on algorithmic probability and sequential decision theory"
    - "reinforcement learning a survey"

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)