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: "tstarbots defeating the cheating level builtin ai in starcraft ii in the full game"
  Abstract: Starcraft II (SC2) is widely considered as the most challenging Real Time Strategy (RTS) game. The underlying challenges include a large observation space, a huge (continuous and infinite) action space, partial observations, simultaneous move for all players, and long horizon delayed rewards for local decisions. To push the frontier of AI research, Deepmind and Blizzard jointly developed the StarCraft II Learning Environment (SC2LE) as a testbench of complex decision making systems. SC2LE provides a few mini games such as MoveToBeacon, CollectMineralShards, and DefeatRoaches, where some AI agents have achieved the performance level of human professional players. However, for full games, the current AI agents are still far from achieving human professional level performance. To bridge this gap, we present two full game AI agents in this paper - the AI agent TStarBot1 is based on deep reinforcement learning over a flat action structure, and the AI agent TStarBot2 is based on hard-coded rules over a hierarchical action structure. Both TStarBot1 and TStarBot2 are able to defeat the built-in AI agents from level 1 to level 10 in a full game (1v1 Zerg-vs-Zerg game on the AbyssalReef map), noting that level 8, level 9, and level 10 are cheating agents with unfair advantages such as full vision on the whole map and resource harvest boosting. To the best of our knowledge, this is the first public work to investigate AI agents that can defeat the built-in AI in the StarCraft II full game.

Connected through hyperedge HE-1 (induced by source paper "deep reinforcement learning for cyber security"):
  with members:
    - "deep reinforcement learning for detecting malicious websites"
    - "manipulating soft tissues by deep reinforcement learning for autonomous robotic surgery"
    - "multi agent deep reinforcement learning with human strategies"
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "a new tensioning method using deep reinforcement learning for surgical pattern cutting"
    - "relational deep reinforcement learning"
    - "variance reduction for policy gradient with action dependent factorized baselines"
    - "robust deep reinforcement learning for security and safety in autonomous vehicle systems"

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 "continual reinforcement learning with diversity exploration and adversarial self correction"):
  with members:
    - "marathon environments multi agent continuous control benchmarks in a modern video game engine"
    - "deepmind control suite"
    - "deep reinforcement learning for autonomous driving"
    - "an introduction to deep reinforcement learning"
    - "building generalizable agents with a realistic and rich 3d environment"
    - "diversity is all you need learning skills without a reward function"
    - "learning to learn without forgetting by maximizing transfer and minimizing interference"
    - "variational option discovery algorithms"

Connected through hyperedge HE-4 (induced by source paper "the starcraft multi agent challenge"):
  with members:
    - "qtran learning to factorize with transformation for cooperative multi agent reinforcement learning"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "starcraft micromanagement with reinforcement learning and curriculum transfer learning"
    - "multi agent common knowledge reinforcement learning"
    - "value propagation networks"
    - "mean field multi agent reinforcement learning"
    - "on reinforcement learning for full length game of starcraft"
    - "multi goal reinforcement learning challenging robotics environments and request for research"

Connected through hyperedge HE-5 (induced by source paper "prolonets neural encoding human experts domain knowledge to warm start reinforcement learning"):
  with members:
    - "tripping through time efficient localization of activities in videos"
    - "imitating latent policies from observation"
    - "exploration by random network distillation"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "learning dexterous in hand manipulation"
    - "adaptive neural trees"
    - "fast policy learning through imitation and reinforcement"
    - "deep reinforcement learning that matters"

Connected through hyperedge HE-6 (induced by source paper "hierarchical decision making by generating and following natural language instructions"):
  with members:
    - "relational deep reinforcement learning"
    - "speaker follower models for vision and language navigation"
    - "hierarchical macro strategy model for moba game ai"
    - "unified pragmatic models for generating and following instructions"
    - "elf an extensive lightweight and flexible research platform for real time strategy games"
    - "starcraft ii a new challenge for reinforcement learning"
    - "parlai a dialog research software platform"
    - "zero shot task generalization with multi task deep reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "learning a behavioral repertoire from demonstrations"):
  with members:
    - "diversity is all you need learning skills without a reward function"
    - "imitation learning with concurrent actions in 3d games"
    - "msc a dataset for macro management in starcraft ii"
    - "hindsight experience replay"
    - "teacher student curriculum learning"
    - "learning macromanagement in starcraft from replays using deep learning"
    - "improving exploration in evolution strategies for deep reinforcement learning via a population of novelty seeking agents"
    - "deep learning for video game playing"

Connected through hyperedge HE-8 (induced by source paper "neural encoding human experts domain knowledge to warm start reinforcement learning"):
  with members:
    - "tripping through time efficient localization of activities in videos"
    - "imitating latent policies from observation"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "learning dexterous in hand manipulation"
    - "fast policy learning through imitation and reinforcement"
    - "lifelong learning with dynamically expandable networks"
    - "learning complex dexterous manipulation with deep reinforcement learning and demonstrations"
    - "proximal policy optimization algorithms"

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)