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: "modular architecture for starcraft ii with deep reinforcement learning"
  Abstract: We present a novel modular architecture for StarCraft II AI. The architecture splits responsibilities between multiple modules that each control one aspect of the game, such as build-order selection or tactics. A centralized scheduler reviews macros suggested by all modules and decides their order of execution. An updater keeps track of environment changes and instantiates macros into series of executable actions. Modules in this framework can be optimized independently or jointly via human design, planning, or reinforcement learning. We apply deep reinforcement learning techniques to training two out of six modules of a modular agent with self-play, achieving 94% or 87% win rates against the "Harder" (level 5) built-in Blizzard bot in Zerg vs. Zerg matches, with or without fog-of-war.

Connected through hyperedge HE-1 (induced by source paper "why build an assistant in minecraft"):
  with members:
    - "craftassist a framework for dialogue enabled interactive agents"
    - "habitat a platform for embodied ai research"
    - "learning from dialogue after deployment feed yourself chatbot"
    - "babyai a platform to study the sample efficiency of grounded language learning"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "babyai first steps towards grounded language learning with a human in the loop"
    - "towards deep conversational recommendations"
    - "learning to ask questions in open domain conversational systems with typed decoders"

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 "growing action spaces"):
  with members:
    - "the starcraft multi agent challenge"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "mix match agent curricula for reinforcement learning"
    - "rainbow combining improvements in deep reinforcement learning"
    - "automated curriculum learning for neural networks"
    - "reverse curriculum generation for reinforcement learning"
    - "starcraft ii a new challenge for reinforcement learning"

Connected through hyperedge HE-4 (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"
    - "on reinforcement learning for full length game of starcraft"
    - "tunneling neural perception and logic reasoning through abductive learning"

Connected through hyperedge HE-5 (induced by source paper "join query optimization with deep reinforcement learning algorithms"):
  with members:
    - "neo a learned query optimizer"
    - "learning state representations for query optimization with deep reinforcement learning"
    - "deep reinforcement learning for join order enumeration"
    - "learning to optimize join queries with deep reinforcement learning"
    - "towards a hands free query optimizer through deep learning"
    - "rainbow combining improvements in deep reinforcement learning"
    - "proximal policy optimization algorithms"
    - "wisedb a learning based workload management advisor for cloud databases"

Connected through hyperedge HE-6 (induced by source paper "comparing observation and action representations for deep reinforcement learning in microrts"):
  with members:
    - "the starcraft multi agent challenge"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "tstarbots defeating the cheating level builtin ai in starcraft ii in the full game"
    - "counterfactual multi agent policy gradients"
    - "elf an extensive lightweight and flexible research platform for real time strategy games"
    - "starcraft ii a new challenge for reinforcement learning"
    - "curiosity driven exploration by self supervised prediction"
    - "value decomposition networks for cooperative multi agent 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)