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: "continual match based training in pommerman technical report"
  Abstract: Continual learning is the ability of agents to improve their capacities throughout multiple tasks continually. While recent works in the literature of continual learning mostly focused on developing either particular loss functions or specialized structures of neural network explaining the episodic memory or neural plasticity, we study continual learning from the perspective of the training mechanism. Specifically, we propose a COnitnual Match BAsed Training (COMBAT) framework for training a population of advantage-actor-critic (A2C) agents in Pommerman, a partially observable multi-agent environment with no communication. Following the COMBAT framework, we trained an agent, namely, Navocado, that won the title of the top 1 learning agent in the NeurIPS 2018 Pommerman Competition. Two critical features of our agent are worth mentioning. Firstly, our agent did not learn from any demonstrations. Secondly, our agent is highly reproducible. As a technical report, we articulate the design of state space, action space, reward, and most importantly, the COMBAT framework for our Pommerman agent. We show in the experiments that Pommerman is a perfect environment for studying continual learning, and the agent can improve its performance by continually learning new skills without forgetting the old ones. Finally, the result in the Pommerman Competition verifies the robustness of our agent when competing with various opponents.

Connected through hyperedge HE-1 (induced by source paper "accelerating training in pommerman with imitation and reinforcement learning"):
  with members:
    - "agent modeling as auxiliary task for deep reinforcement learning"
    - "real time tree search with pessimistic scenarios"
    - "skynet a top deep rl agent in the inaugural pommerman team competition"
    - "safer deep rl with shallow mcts a case study in pommerman"
    - "backplay man muss immer umkehren"
    - "deep multi agent reinforcement learning with relevance graphs"
    - "sim to real reinforcement learning for deformable object manipulation"
    - "pommerman a multi agent playground"

Connected through hyperedge HE-2 (induced by source paper "on hard exploration for reinforcement learning a case study in pommerman"):
  with members:
    - "go explore a new approach for hard exploration problems"
    - "model free model based and general intelligence"
    - "theoretical impediments to machine learning with seven sparks from the causal revolution"
    - "backplay man muss immer umkehren"
    - "using monte carlo tree search as a demonstrator within asynchronous deep rl"
    - "treeqn and atreec differentiable tree structured models for deep reinforcement learning"
    - "human level intelligence or animal like abilities"
    - "proximal policy optimization algorithms"

Connected through hyperedge HE-3 (induced by source paper "real time tree search with pessimistic scenarios"):
  with members:
    - "a survey and critique of multiagent deep reinforcement learning"
    - "backplay man muss immer umkehren"
    - "multi agent reinforcement learning a report on challenges and approaches"
    - "pommerman a multi agent playground"
    - "how to combine tree search methods in reinforcement learning"
    - "feedback based tree search for reinforcement learning"
    - "thinking fast and slow with deep learning and tree search"
    - "the ff planning system fast plan generation through heuristic search"

Connected through hyperedge HE-4 (induced by source paper "iterative update and unified representation for multi agent reinforcement learning"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "improved training of wasserstein gans"
    - "population based training of neural networks"
    - "multiagent bidirectionally coordinated nets emergence of human level coordination in learning to play starcraft combat games"
    - "value decomposition networks for cooperative multi agent learning"
    - "continuous control with deep reinforcement learning"

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)