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: "a monte carlo aixi approximation"
  Abstract: This paper introduces a principled approach for the design of a scalable general reinforcement learning agent. Our approach is based on a direct approximation of AIXI, a Bayesian optimality notion for general reinforcement learning agents. Previously, it has been unclear whether the theory of AIXI could motivate the design of practical algorithms. We answer this hitherto open question in the affirmative, by providing the first computationally feasible approximation to the AIXI agent. To develop our approximation, we introduce a new Monte-Carlo Tree Search algorithm along with an agent-specific extension to the Context Tree Weighting algorithm. Empirically, we present a set of encouraging results on a variety of stochastic and partially observable domains. We conclude by proposing a number of directions for future research.

Connected through hyperedge HE-1 (induced by source paper "influence aware memory for deep reinforcement learning"):
  with members:
    - "a sufficient statistic for influence in structured multiagent environments"
    - "attention is all you need"
    - "proximal policy optimization algorithms"
    - "show attend and tell neural image caption generation with visual attention"
    - "deep attention recurrent q network"
    - "deep recurrent q learning for partially observable mdps"
    - "the arcade learning environment an evaluation platform for general agents"
    - "pegasus a policy search method for large mdps and pomdps"

Connected through hyperedge HE-2 (induced by source paper "efficient planning under partial observability with unnormalized q functions and spectral learning"):
  with members:
    - "efficient learning and planning with compressed predictive states"
    - "predictive state representations a new theory for modeling dynamical systems"
    - "feature hashing for large scale multitask learning"
    - "closing the learning planning loop with predictive state representations"

Connected through hyperedge HE-3 (induced by source paper "learning the reward function for a misspecified model"):
  with members:
    - "self supervised visual planning with temporal skip connections"
    - "value prediction network"
    - "recurrent environment simulators"
    - "deep learning for reward design to improve monte carlo tree search in atari games"
    - "self correcting models for model based reinforcement learning"
    - "value iteration networks"
    - "scheduled sampling for sequence prediction with recurrent neural networks"
    - "agnostic system identification for model based reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "vision system for agi problems and directions"):
  with members:
    - "global pose estimation with an attention based recurrent network"
    - "dynamic routing between capsules"
    - "building machines that learn and think like people"
    - "neural turing machines"

Connected through hyperedge HE-5 (induced by source paper "solving tree problems with category theory"):
  with members:
    - "general problem solving with category theory"
    - "visual attribute transfer through deep image analogy"
    - "a framework for searching for general artificial intelligence"

Connected through hyperedge HE-6 (induced by source paper "computable variants of aixi which are more powerful than aixitl"):
  with members:
    - "universal algorithmic intelligence a mathematical top down approach"

Connected through hyperedge HE-7 (induced by source paper "universal reinforcement learning algorithms survey and experiments"):
  with members:
    - "count based exploration in feature space for reinforcement learning"
    - "generalised discount functions applied to a monte carlo aimu implementation"
    - "nonparametric general reinforcement learning"
    - "thompson sampling is asymptotically optimal in general environments"
    - "vime variational information maximizing exploration"
    - "unifying count based exploration and intrinsic motivation"
    - "bad universal priors and notions of optimality"
    - "on the computability of solomonoff induction and knowledge seeking"

Connected through hyperedge HE-8 (induced by source paper "loss bounds and time complexity for speed priors"):
  with members:
    - "on the computability of solomonoff induction and knowledge seeking"
    - "non equivalence of universal priors"
    - "optimal ordered problem solver"

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)