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: "on reinforcement learning for full length game of starcraft"
  Abstract: StarCraft II poses a grand challenge for reinforcement learning. The main difficulties of it include huge state and action space and a long-time horizon. In this paper, we investigate a hierarchical reinforcement learning approach for StarCraft II. The hierarchy involves two levels of abstraction. One is the macro-action automatically extracted from expert's trajectories, which reduces the action space in an order of magnitude yet remains effective. The other is a two-layer hierarchical architecture which is modular and easy to scale, enabling a curriculum transferring from simpler tasks to more complex tasks. The reinforcement training algorithm for this architecture is also investigated. On a 64x64 map and using restrictive units, we achieve a winning rate of more than 99\% against the difficulty level-1 built-in AI. Through the curriculum transfer learning algorithm and a mixture of combat model, we can achieve over 93\% winning rate of Protoss against the most difficult non-cheating built-in AI (level-7) of Terran, training within two days using a single machine with only 48 CPU cores and 8 K40 GPUs. It also shows strong generalization performance, when tested against never seen opponents including cheating levels built-in AI and all levels of Zerg and Protoss built-in AI. We hope this study could shed some light on the future research of large-scale reinforcement learning.

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 "on multi agent learning in team sports games"):
  with members:
    - "winning isn t everything training human like agents for playtesting and game ai"
    - "skynet a top deep rl agent in the inaugural pommerman team competition"
    - "safer deep rl with shallow mcts a case study in pommerman"
    - "learning latent plans from play"
    - "a survey and critique of multiagent deep reinforcement learning"
    - "multi agent adversarial inverse reinforcement learning"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "learning dexterous in hand manipulation"

Connected through hyperedge HE-3 (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"
    - "tstarbots defeating the cheating level builtin ai in starcraft ii in the full game"
    - "multi goal reinforcement learning challenging robotics environments and request for research"

Connected through hyperedge HE-4 (induced by source paper "winning isn t everything training human like agents for playtesting and game ai"):
  with members:
    - "a survey and critique of multiagent deep reinforcement learning"
    - "exploring gameplay with ai agents"
    - "automated playtesting with procedural personas through mcts with evolved heuristics"
    - "rainbow combining improvements in deep reinforcement learning"
    - "starcraft ii a new challenge for reinforcement learning"
    - "procedural content generation via machine learning pcgml"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "proximal policy optimization algorithms"

Connected through hyperedge HE-5 (induced by source paper "a narration based reward shaping approach using grounded natural language commands"):
  with members:
    - "from language to goals inverse reinforcement learning for vision based instruction following"
    - "grounding natural language commands to starcraft ii game states for narration guided reinforcement learning"
    - "mapping instructions to actions in 3d environments with visual goal prediction"
    - "follownet robot navigation by following natural language directions with deep reinforcement learning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "following high level navigation instructions on a simulated quadcopter with imitation learning"
    - "reward learning from narrated demonstrations"
    - "accurately and efficiently interpreting human robot instructions of varying granularities"

Connected through hyperedge HE-6 (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"
    - "macro action selection with deep reinforcement learning in starcraft"
    - "world models"
    - "hierarchical macro strategy model for moba game ai"
    - "tstarbots defeating the cheating level builtin ai in starcraft ii in the full game"
    - "modular architecture for starcraft ii with deep reinforcement learning"
    - "tunneling neural perception and logic reasoning through abductive learning"

Connected through hyperedge HE-7 (induced by source paper "grounding natural language commands to starcraft ii game states for narration guided reinforcement learning"):
  with members:
    - "efficiently combining human demonstrations and interventions for safe training of autonomous systems in real time"
    - "follownet robot navigation by following natural language directions with deep reinforcement learning"
    - "following high level navigation instructions on a simulated quadcopter with imitation learning"
    - "reward learning from narrated demonstrations"
    - "accurately and efficiently interpreting human robot instructions of varying granularities"
    - "starcraft ii a new challenge for reinforcement learning"
    - "deep tamer interactive agent shaping in high dimensional state spaces"
    - "listen attend and walk neural mapping of navigational instructions to action sequences"

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.LG(Machine Learning)