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: "torchcraft a library for machine learning research on real time strategy games"
  Abstract: We present TorchCraft, a library that enables deep learning research on Real-Time#R##N#Strategy (RTS) games such as StarCraft: Brood War, by making it easier to control these#R##N#games from a machine learning framework, here Torch [9]. This white paper argues for#R##N#using RTS games as a benchmark for AI research, and describes the design and components#R##N#of TorchCraft.

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 "multi target embodied question answering"):
  with members:
    - "gibson env real world perception for embodied agents"
    - "blindfold baselines for embodied qa"
    - "mattnet modular attention network for referring expression comprehension"
    - "building generalizable agents with a realistic and rich 3d environment"
    - "neural modular control for embodied question answering"
    - "minos multimodal indoor simulator for navigation in complex environments"
    - "inferring and executing programs for visual reasoning"
    - "home a household multimodal environment"

Connected through hyperedge HE-3 (induced by source paper "autonomous industrial management via reinforcement learning self learning agents for decision making a review"):
  with members:
    - "autonomous agents modelling other agents a comprehensive survey and open problems"
    - "world models"
    - "a web page classifier library based on random image content analysis using deep learning"
    - "imitation learning with concurrent actions in 3d games"
    - "one shot visual imitation learning via meta learning"
    - "elf an extensive lightweight and flexible research platform for real time strategy games"
    - "deep reinforcement learning a brief survey"
    - "domain randomization for transferring deep neural networks from simulation to the real world"

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"
    - "tstarbots defeating the cheating level builtin ai in starcraft ii in the full game"
    - "on reinforcement learning for full length game of starcraft"

Connected through hyperedge HE-5 (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"
    - "modular architecture for starcraft ii with deep reinforcement learning"
    - "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"

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"
    - "tstarbots defeating the cheating level builtin ai in starcraft ii in the full game"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "from video game to real robot the transfer between action spaces"):
  with members:
    - "learning agile and dynamic motor skills for legged robots"
    - "learning dexterous in hand manipulation"
    - "torille learning environment for hand to hand combat"
    - "sim to real learning agile locomotion for quadruped robots"
    - "progressive neural networks"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "reinforcement learning for pivoting task"
    - "starcraft ii a new challenge for reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "neural fictitious self play on elf mini rts"):
  with members:
    - "elf an extensive lightweight and flexible research platform for real time strategy games"
    - "learning with opponent learning awareness"
    - "starcraft ii a new challenge for reinforcement learning"
    - "hybrid reward architecture for reinforcement learning"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "proximal policy optimization algorithms"
    - "deep reinforcement learning from self play in imperfect information games"
    - "asynchronous methods for deep reinforcement 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.LG(Machine Learning)