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: "mapping instructions and visual observations to actions with reinforcement learning"
  Abstract: We propose to directly map raw visual observations and text input to actions for instruction execution. While existing approaches assume access to structured environment representations or use a pipeline of separately trained models, we learn a single model to jointly reason about linguistic and visual input. We use reinforcement learning in a contextual bandit setting to train a neural network agent. To guide the agent's exploration, we use reward shaping with different forms of supervision. Our approach does not require intermediate representations, planning procedures, or training different models. We evaluate in a simulated environment, and show significant improvements over supervised learning and common reinforcement learning variants.

Connected through hyperedge HE-1 (induced by source paper "towards learning a generic agent for vision and language navigation via pre training"):
  with members:
    - "vision and dialog navigation"
    - "tactical rewind self correction via backtracking in vision and language navigation"
    - "lxmert learning cross modality encoder representations from transformers"
    - "unicoder vl a universal encoder for vision and language by cross modal pre training"
    - "vilbert pretraining task agnostic visiolinguistic representations for vision and language tasks"
    - "robust navigation with language pretraining and stochastic sampling"
    - "videobert a joint model for video and language representation learning"
    - "vl bert pre training of generic visual linguistic representations"

Connected through hyperedge HE-2 (induced by source paper "multi view learning for vision and language navigation"):
  with members:
    - "towards learning a generic agent for vision and language navigation via pre training"
    - "tactical rewind self correction via backtracking in vision and language navigation"
    - "vision language navigation with self supervised auxiliary reasoning tasks"
    - "self monitoring navigation agent via auxiliary progress estimation"
    - "the regretful agent heuristic aided navigation through progress estimation"
    - "learning to navigate unseen environments back translation with environmental dropout"
    - "reinforced cross modal matching and self supervised imitation learning for vision language navigation"
    - "touchdown natural language navigation and spatial reasoning in visual street environments"

Connected through hyperedge HE-3 (induced by source paper "exploiting language instructions for interpretable and compositional reinforcement learning"):
  with members:
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "babyai a platform to study the sample efficiency of grounded language learning"
    - "babyai first steps towards grounded language learning with a human in the loop"
    - "building generalizable agents with a realistic and rich 3d environment"
    - "learning to understand goal specifications by modelling reward"
    - "interactive grounded language acquisition and generalization in a 2d world"
    - "reverse curriculum generation for reinforcement learning"
    - "visualisation and diagnostic classifiers reveal how recurrent and recursive neural networks process hierarchical structure"

Connected through hyperedge HE-4 (induced by source paper "retouchdown adding touchdown to streetlearn as a shareable resource for language grounding tasks in street view"):
  with members:
    - "navigation agents for the visually impaired a sidewalk simulator and experiments"
    - "vision language navigation with self supervised auxiliary reasoning tasks"
    - "valan vision and language agent navigation"
    - "effective and general evaluation for instruction conditioned navigation using dynamic time warping"
    - "the streetlearn environment and dataset"
    - "reinforced cross modal matching and self supervised imitation learning for vision language navigation"
    - "talk the walk navigating new york city through grounded dialogue"
    - "touchdown natural language navigation and spatial reasoning in visual street environments"

Connected through hyperedge HE-5 (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"
    - "hierarchical decision making by generating and following natural language instructions"
    - "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"

Connected through hyperedge HE-6 (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-7 (induced by source paper "bayesian relational memory for semantic visual navigation"):
  with members:
    - "a behavioral approach to visual navigation with graph localization networks"
    - "benchmarking classic and learned navigation in complex 3d environments"
    - "probabilistic neural symbolic models for interpretable visual question answering"
    - "scene memory transformer for embodied agents in long horizon tasks"
    - "composable planning with attributes"
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "iterative visual reasoning beyond convolutions"
    - "learning plannable representations with causal infogan"

Connected through hyperedge HE-8 (induced by source paper "non monotonic sequential text generation"):
  with members:
    - "deterministic non autoregressive neural sequence modeling by iterative refinement"
    - "blockwise parallel decoding for deep autoregressive models"
    - "relational inductive biases deep learning and graph networks"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "texygen a benchmarking platform for text generation models"
    - "language gans falling short"
    - "the importance of generation order in language modeling"
    - "fast policy learning through imitation and reinforcement"

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.CL(Computation and Language)