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: "moba a new arena for game ai"
  Abstract: Games have always been popular testbeds for Artificial Intelligence (AI). In the last decade, we have seen the rise of the Multiple Online Battle Arena (MOBA) games, which are the most played games nowadays. In spite of this, there are few works that explore MOBA as a testbed for AI Research. In this paper we present and discuss the main features and opportunities offered by MOBA games to Game AI Research. We describe the various challenges faced along the game and also propose a discrete model that can be used to better understand and explore the game. With this, we aim to encourage the use of MOBA as a novel research platform for Game AI.

Connected through hyperedge HE-1 (induced by source paper "dynamic face video segmentation via reinforcement learning"):
  with members:
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "face mask extraction in video sequence"
    - "architecture search of dynamic cells for semantic video segmentation"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "meta learning transferable active learning policies by deep reinforcement learning"
    - "dynamic video segmentation network"
    - "low latency video semantic segmentation"
    - "light weight refinenet for real time semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "mastering complex control in moba games with deep reinforcement learning"):
  with members:
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "marginal policy gradients a unified family of estimators for bounded action spaces with applications"
    - "an empirical model of large batch training"
    - "hierarchical macro strategy model for moba game ai"
    - "horovod fast and easy distributed deep learning in tensorflow"
    - "feedback based tree search for reinforcement learning"
    - "emergent complexity via multi agent competition"

Connected through hyperedge HE-3 (induced by source paper "finite sample analysis for decentralized batch multi agent reinforcement learning with networked agents"):
  with members:
    - "a multi agent off policy actor critic algorithm for distributed reinforcement learning"
    - "a theoretical analysis of deep q learning"
    - "policy optimization provably converges to nash equilibria in zero sum linear quadratic games"
    - "information theoretic considerations in batch reinforcement learning"
    - "a finite time analysis of temporal difference learning with linear function approximation"
    - "dynamic power distribution system management with a locally connected communication network"
    - "communication efficient distributed reinforcement learning"
    - "distributed equilibrium learning for power network voltage control with a locally connected communication network"

Connected through hyperedge HE-4 (induced by source paper "hierarchical macro strategy model for moba game ai"):
  with members:
    - "elf an extensive lightweight and flexible research platform for real time strategy games"
    - "starcraft ii a new challenge for reinforcement learning"
    - "on the development of intelligent agents for moba games"
    - "proximal policy optimization algorithms"
    - "learning multiagent communication with backpropagation"
    - "learning to communicate to solve riddles with deep distributed recurrent q networks"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-5 (induced by source paper "deep reinforcement learning that matters"):
  with members:
    - "parameter space noise for exploration"
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"
    - "reproducibility of benchmarked deep reinforcement learning tasks for continuous control"
    - "starcraft ii a new challenge for reinforcement learning"
    - "third person imitation learning"
    - "towards generalization and simplicity in continuous control"
    - "interpolated policy gradient merging on policy and off policy gradient estimation for deep reinforcement learning"
    - "revisiting the arcade learning environment evaluation protocols and open problems for general agents"

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)