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: "monte carlo tree search with heuristic evaluations using implicit minimax backups"
  Abstract: Monte Carlo Tree Search (MCTS) has improved the performance of game engines in domains such as Go, Hex, and general game playing. MCTS has been shown to outperform classic alpha-beta search in games where good heuristic evaluations are difficult to obtain. In recent years, combining ideas from traditional minimax search in MCTS has been shown to be advantageous in some domains, such as Lines of Action, Amazons, and Breakthrough. In this paper, we propose a new way to use heuristic evaluations to guide the MCTS search by storing the two sources of information, estimated win rates and heuristic evaluations, separately. Rather than using the heuristic evaluations to replace the playouts, our technique backs them up implicitly during the MCTS simulations. These minimax values are then used to guide future simulations. We show that using implicit minimax backups leads to stronger play performance in Kalah, Breakthrough, and Lines of Action.

Connected through hyperedge HE-1 (induced by source paper "self supervised deep active accelerated mri"):
  with members:
    - "modl model based deep learning architecture for inverse problems"
    - "deep residual learning for accelerated mri using magnitude and phase networks"
    - "deep convolutional neural network for inverse problems in imaging"
    - "learning a variational network for reconstruction of accelerated mri data"
    - "unified theory for recovery of sparse signals in a general transform domain"
    - "tensorflow a system for large scale machine learning"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-2 (induced by source paper "multiple policy value monte carlo tree search"):
  with members:
    - "elf opengo an analysis and open reimplementation of alphazero"
    - "better computer go player with neural network and long term prediction"
    - "deep residual learning for image recognition"
    - "teaching deep convolutional neural networks to play go"

Connected through hyperedge HE-3 (induced by source paper "non stationary markov decision processes a worst case approach using model based reinforcement learning"):
  with members:
    - "lipschitz continuity in model based reinforcement learning"
    - "distributional reinforcement learning with quantile regression"
    - "empirical evaluation of a q learning algorithm for model free autonomous soaring"
    - "multi armed bandits in metric spaces"

Connected through hyperedge HE-4 (induced by source paper "how to combine tree search methods in reinforcement learning"):
  with members:
    - "multiple step greedy policies in online and approximate reinforcement learning"
    - "is q learning provably efficient"
    - "feedback based tree search for reinforcement learning"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "asynchronous methods for deep reinforcement learning"
    - "giraffe using deep reinforcement learning to play chess"

Connected through hyperedge HE-5 (induced by source paper "asymmetric move selection strategies in monte carlo tree search minimizing the simple regret at max nodes"):
  with members:
    - "better computer go player with neural network and long term prediction"
    - "bandit algorithms for tree search"
    - "mcts based on simple regret"

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)