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: "hierarchical decision making by generating and following natural language instructions"
  Abstract: We explore using latent natural language instructions as an expressive and compositional representation of complex actions for hierarchical decision making. Rather than directly selecting micro-actions, our agent first generates a latent plan in natural language, which is then executed by a separate model. We introduce a challenging real-time strategy game environment in which the actions of a large number of units must be coordinated across long time scales. We gather a dataset of 76 thousand pairs of instructions and executions from human play, and train instructor and executor models. Experiments show that models using natural language as a latent variable significantly outperform models that directly imitate human actions. The compositional structure of language proves crucial to its effectiveness for action representation. We also release our code, models and data.

Connected through hyperedge HE-1 (induced by source paper "i love your chain mail making knights smile in a fantasy game world open domain goal orientated dialogue agents"):
  with members:
    - "real time inference in multi sentence tasks with deep pretrained transformers"
    - "acute eval improved dialogue evaluation with optimized questions and multi turn comparisons"
    - "learning to speak and act in a fantasy text adventure game"
    - "countering language drift via visual grounding"
    - "deep reinforcement learning for modeling chit chat dialog with discrete attributes"
    - "target guided open domain conversation"
    - "learning semantic textual similarity from conversations"
    - "textworld a learning environment for text based games"

Connected through hyperedge HE-2 (induced by source paper "language is power representing states using natural language in reinforcement learning"):
  with members:
    - "action assembly sparse imitation learning for text based games with combinatorial action spaces"
    - "the natural language of actions"
    - "adaptive discretization for episodic reinforcement learning in metric spaces"
    - "textworld a learning environment for text based games"
    - "a comprehensive survey of deep learning for image captioning"
    - "explainable artificial intelligence understanding visualizing and interpreting deep learning models"
    - "emergence of invariance and disentanglement in deep representations"
    - "do latent tree learning models identify meaningful structure in sentences"

Connected through hyperedge HE-3 (induced by source paper "learning from explanations with neural execution tree"):
  with members:
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "explain yourself leveraging language models for commonsense reasoning"
    - "generalize symbolic knowledge with neural rule engine"
    - "e snli natural language inference with natural language explanations"
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "heterogeneous supervision for relation extraction a representation learning approach"
    - "constructing datasets for multi hop reading comprehension across documents"

Connected through hyperedge HE-4 (induced by source paper "following instructions by imagining and reaching visual goals"):
  with members:
    - "embodied language grounding with implicit 3d visual feature representations"
    - "language as an abstraction for hierarchical deep reinforcement learning"
    - "visual reinforcement learning with imagined goals"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "learning to understand goal specifications by modelling reward"
    - "representation learning for grounded spatial reasoning"
    - "mapping instructions and visual observations to actions with reinforcement learning"

Connected through hyperedge HE-5 (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"
    - "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"
    - "flipdial a generative model for two way visual dialogue"

Connected through hyperedge HE-6 (induced by source paper "executing instructions in situated collaborative interactions"):
  with members:
    - "meetup a corpus of joint activity dialogues in a visual environment"
    - "a natural language corpus of common grounding under continuous and partially observable context"
    - "learning to map context dependent sentences to executable formal queries"
    - "talk the walk navigating new york city through grounded dialogue"
    - "touchdown natural language navigation and spatial reasoning in visual street environments"
    - "decoupling strategy and generation in negotiation dialogues"
    - "situated mapping of sequential instructions to actions with single step reward observation"
    - "mapping instructions to actions in 3d environments with visual goal prediction"

Connected through hyperedge HE-7 (induced by source paper "learning to annotate modularizing data augmentation for text classifiers with natural language explanations"):
  with members:
    - "learning dual retrieval module for semi supervised relation extraction"
    - "moss end to end dialog system framework with modular supervision"
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "generalize symbolic knowledge with neural rule engine"
    - "e snli natural language inference with natural language explanations"
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "heterogeneous supervision for relation extraction a representation learning approach"

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)