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: "chalet cornell house agent learning environment"
  Abstract: We present CHALET, a 3D house simulator with support for navigation and manipulation. CHALET includes 58 rooms and 10 house configuration, and allows to easily create new house and room layouts. CHALET supports a range of common household activities, including moving objects, toggling appliances, and placing objects inside closeable containers. The environment and actions available are designed to create a challenging domain to train and evaluate autonomous agents, including for tasks that combine language, vision, and planning in a dynamic environment.

Connected through hyperedge HE-1 (induced by source paper "learning to follow directions in street view"):
  with members:
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "learning visual question answering by bootstrapping hard attention"
    - "global pose estimation with an attention based recurrent network"
    - "talk the walk navigating new york city through grounded dialogue"
    - "touchdown natural language navigation and spatial reasoning in visual street environments"
    - "gibson env real world perception for embodied agents"
    - "a critical investigation of deep reinforcement learning for navigation"

Connected through hyperedge HE-2 (induced by source paper "arena a general evaluation platform and building toolkit for multi agent intelligence"):
  with members:
    - "neural mmo a massively multiagent game environment for training and evaluating intelligent agents"
    - "marathon environments multi agent continuous control benchmarks in a modern video game engine"
    - "emergent coordination through competition"
    - "autocurricula and the emergence of innovation from social interaction a manifesto for multi agent intelligence research"
    - "a survey and critique of multiagent deep reinforcement learning"
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "habitat a platform for embodied ai research"
    - "open ended learning in symmetric zero sum games"

Connected through hyperedge HE-3 (induced by source paper "a survey of reinforcement learning informed by natural language"):
  with members:
    - "assessing bert s syntactic abilities"
    - "learning to generalize from sparse and underspecified rewards"
    - "using natural language for reward shaping in reinforcement learning"
    - "from language to goals inverse reinforcement learning for vision based instruction following"
    - "hierarchical decision making by generating and following natural language instructions"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "babyai a platform to study the sample efficiency of grounded language learning"
    - "an algorithmic perspective on imitation learning"

Connected through hyperedge HE-4 (induced by source paper "splitnet sim2sim and task2task transfer for embodied visual navigation"):
  with members:
    - "divis domain invariant visual servoing for collision free goal reaching"
    - "habitat a platform for embodied ai research"
    - "mid level visual representations improve generalization and sample efficiency for learning active tasks"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "taskonomy disentangling task transfer learning"
    - "gibson env real world perception for embodied agents"
    - "on evaluation of embodied navigation agents"
    - "driving policy transfer via modularity and abstraction"

Connected through hyperedge HE-5 (induced by source paper "habitat a platform for embodied ai research"):
  with members:
    - "the replica dataset a digital replica of indoor spaces"
    - "benchmarking classic and learned navigation in complex 3d environments"
    - "pyrobot an open source robotics framework for research and benchmarking"
    - "learning agile and dynamic motor skills for legged robots"
    - "to learn or not to learn analyzing the role of learning for navigation in virtual environments"
    - "virtualhome simulating household activities via programs"
    - "mid level visual representations improve generalization and sample efficiency for learning active tasks"
    - "gibson env real world perception for embodied agents"

Connected through hyperedge HE-6 (induced by source paper "sim real joint reinforcement transfer for 3d indoor navigation"):
  with members:
    - "dm gan dynamic memory generative adversarial networks for text to image synthesis"
    - "video to video synthesis"
    - "adversarial learning with local coordinate coding"
    - "filter pruning via geometric median for deep convolutional neural networks acceleration"
    - "guided feature transformation gft a neural language grounding module for embodied agents"
    - "on evaluation of embodied navigation agents"
    - "building generalizable agents with a realistic and rich 3d environment"
    - "taking a closer look at domain shift category level adversaries for semantics consistent domain adaptation"

Connected through hyperedge HE-7 (induced by source paper "to learn or not to learn analyzing the role of learning for navigation in virtual environments"):
  with members:
    - "benchmarking classic and learned navigation in complex 3d environments"
    - "habitat a platform for embodied ai research"
    - "codeslam learning a compact optimisable representation for dense visual slam"
    - "gibson env real world perception for embodied agents"
    - "on evaluation of embodied navigation agents"
    - "minos multimodal indoor simulator for navigation in complex environments"
    - "unifying map and landmark based representations for visual navigation"
    - "an analysis of visual question answering algorithms"

Connected through hyperedge HE-8 (induced by source paper "what should i do now marrying reinforcement learning and symbolic planning"):
  with members:
    - "hyp despot a hybrid parallel algorithm for online planning under uncertainty"
    - "fusion volumetric object level slam"
    - "gibson env real world perception for embodied agents"
    - "on evaluation of embodied navigation agents"
    - "building generalizable agents with a realistic and rich 3d environment"
    - "shifting the baseline single modality performance on visual navigation qa"
    - "neural modular control for embodied question answering"
    - "gated attention architectures for task oriented language grounding"

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)