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: "learning with latent language"
  Abstract: The named concepts and compositional operators present in natural language provide a rich source of information about the kinds of abstractions humans use to navigate the world. Can this linguistic background knowledge improve the generality and efficiency of learned classifiers and control policies? This paper aims to show that using the space of natural language strings as a parameter space is an effective way to capture natural task structure. In a pretraining phase, we learn a language interpretation model that transforms inputs (e.g. images) into outputs (e.g. labels) given natural language descriptions. To learn a new concept (e.g. a classifier), we search directly in the space of descriptions to minimize the interpreter's loss on training examples. Crucially, our models do not require language data to learn these concepts: language is used only in pretraining to impose structure on subsequent learning. Results on image classification, text editing, and reinforcement learning show that, in all settings, models with a linguistic parameterization outperform those without.

Connected through hyperedge HE-1 (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-2 (induced by source paper "language as an abstraction for hierarchical deep reinforcement learning"):
  with members:
    - "skew fit state covering self supervised reinforcement learning"
    - "from language to goals inverse reinforcement learning for vision based instruction following"
    - "a survey of reinforcement learning informed by natural language"
    - "hierarchical reinforcement learning with hindsight"
    - "temporal difference models model free deep rl for model based control"
    - "babyai first steps towards grounded language learning with a human in the loop"
    - "near optimal representation learning for hierarchical reinforcement learning"
    - "visual reinforcement learning with imagined goals"

Connected through hyperedge HE-3 (induced by source paper "state reification networks improving generalization by modeling the distribution of hidden representations"):
  with members:
    - "adversarial patch"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "adversarial spheres"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "state denoised recurrent neural networks"
    - "adversarially robust generalization requires more data"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"

Connected through hyperedge HE-4 (induced by source paper "shaping visual representations with language for few shot classification"):
  with members:
    - "baby steps towards few shot learning with multiple semantics"
    - "using natural language for reward shaping in reinforcement learning"
    - "explain yourself leveraging language models for commonsense reasoning"
    - "a closer look at few shot classification"
    - "adaptive cross modal few shot learning"
    - "grounding visual explanations"
    - "e snli natural language inference with natural language explanations"
    - "learning to understand goal specifications by modelling reward"

Connected through hyperedge HE-5 (induced by source paper "rtfm generalising to novel environment dynamics via reading"):
  with members:
    - "language as an abstraction for hierarchical deep reinforcement learning"
    - "torchbeast a pytorch platform for distributed rl"
    - "a survey of reinforcement learning informed by natural language"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "speaker follower models for vision and language navigation"
    - "global locally self attentive dialogue state tracker"
    - "guiding policies with language via meta learning"
    - "end to end neural coreference resolution"

Connected through hyperedge HE-6 (induced by source paper "guiding policies with language via meta learning"):
  with members:
    - "variational inverse control with events a general framework for data driven reward definition"
    - "few shot goal inference for visuomotor learning and planning"
    - "shared autonomy via deep reinforcement learning"
    - "learning a prior over intent via meta inverse reinforcement learning"
    - "learning to adapt meta learning for model based control"
    - "representation learning for grounded spatial reasoning"
    - "learning to learn meta critic networks for sample efficient learning"
    - "prototypical networks for few shot learning"

Connected through hyperedge HE-7 (induced by source paper "state denoised recurrent neural networks"):
  with members:
    - "stronger generalization bounds for deep nets via a compression approach"
    - "learning longer term dependencies in rnns with auxiliary losses"
    - "the consciousness prior"
    - "adaptive computation time for recurrent neural networks"
    - "improving the robustness of deep neural networks via stability training"
    - "gated feedback recurrent neural networks"

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)