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: "macro action selection with deep reinforcement learning in starcraft"
  Abstract: StarCraft (SC) is one of the most popular and successful Real Time Strategy (RTS) games. In recent years, SC is also considered as a testbed for AI research, due to its enormous state space, hidden information, multi-agent collaboration and so on. Thanks to the annual AIIDE and CIG competitions, a growing number of bots are proposed and being continuously improved. However, a big gap still remains between the top bot and the professional human players. One vital reason is that current bots mainly rely on predefined rules to perform macro actions. These rules are not scalable and efficient enough to cope with the large but partially observed macro state space in SC. In this paper, we propose a DRL based framework to do macro action selection. Our framework combines the reinforcement learning approach Ape-X DQN with Long-Short-Term-Memory (LSTM) to improve the macro action selection in bot. We evaluate our bot, named as LastOrder, on the AIIDE 2017 StarCraft AI competition bots set. Our bot achieves overall 83% win-rate, outperforming 26 bots in total 28 entrants.

Connected through hyperedge HE-1 (induced by source paper "defoggan predicting hidden information in the starcraft fog of war with generative adversarial nets"):
  with members:
    - "single image haze removal using conditional wasserstein generative adversarial networks"
    - "edgeconnect generative image inpainting with adversarial edge learning"
    - "forward modeling for partial observation strategy games a starcraft defogger"
    - "clear the fog combat value assessment in incomplete information games with convolutional encoder decoders"
    - "learning macromanagement in starcraft from replays using deep learning"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "improved training of wasserstein gans"
    - "stardata a starcraft ai research dataset"

Connected through hyperedge HE-2 (induced by source paper "convergent policy optimization for safe reinforcement learning"):
  with members:
    - "neural proximal trust region policy optimization attains globally optimal policy"
    - "provably efficient q learning with low switching cost"
    - "on the global convergence of actor critic a case for linear quadratic regulator with ergodic cost"
    - "neural temporal difference learning converges to global optima"
    - "neural policy gradient methods global optimality and rates of convergence"
    - "stochastic successive convex approximation for non convex constrained stochastic optimization"
    - "learning safe policies with expert guidance"
    - "communication efficient distributed reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "efficient reinforcement learning with a thought game for starcraft"):
  with members:
    - "accuracy based curriculum learning in deep reinforcement learning"
    - "relational deep reinforcement learning"
    - "world models"
    - "hierarchical macro strategy model for moba game ai"
    - "tstarbots defeating the cheating level builtin ai in starcraft ii in the full game"
    - "on reinforcement learning for full length game of starcraft"
    - "modular architecture for starcraft ii with deep reinforcement learning"
    - "tunneling neural perception and logic reasoning through abductive 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)