human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "a philosophical treatise of universal induction"):
  Abstract: Understanding inductive reasoning is a problem that has engaged mankind for thousands of years. This problem is relevant to a wide range of fields and is integral to the philosophy of science. It has been tackled by many great minds ranging from philosophers to scientists to mathematicians, and more recently computer scientists. In this article we argue the case for Solomonoff Induction, a formal inductive framework which combines algorithmic information theory with the Bayesian framework. Although it achieves excellent theoretical results and is based on solid philosophical foundations, the requisite technical knowledge necessary for understanding this framework has caused it to remain largely unknown and unappreciated in the wider scientific community. The main contribution of this article is to convey Solomonoff induction and its related concepts in a generally accessible form with the aim of bridging this current technical gap. In the process we examine the major historical contributions that have led to the formulation of Solomonoff Induction as well as criticisms of Solomonoff and induction in general. In particular we examine how Solomonoff induction addresses many issues that have plagued other inductive systems, such as the black ravens paradox and the confirmation problem, and compare this approach with other recent approaches.

Includes members:
  Member "a complete theory of everything will be subjective":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "when and why metaheuristics researchers can ignore no free lunch theorems")
  Member "a monte carlo aixi approximation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "influence aware memory for deep reinforcement learning")
  Member "open problems in universal induction intelligence":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "universal reinforcement learning algorithms survey and experiments")
  Member "algorithmic information theory a brief non technical guide to the field":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "algorithmic randomness as foundation of inductive reasoning and artificial intelligence")
  Member "algorithmic complexity bounds on future prediction errors":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "plain stopping time and conditional complexities revisited")
  Member "universal intelligence a definition of machine intelligence":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "intelligence physics and information the tradeoff between accuracy and simplicity in machine learning")
  Member "similarity of objects and the meaning of words":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "open problems in universal induction intelligence")
  Member "adaptive online prediction by following the perturbed leader":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "online non convex learning following the perturbed leader is optimal")

Question: Please tell me which class the source paper 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)