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 to win by reading manuals in a monte carlo framework"
  Abstract: Domain knowledge is crucial for effective performance in autonomous control systems. Typically, human effort is required to encode this knowledge into a control algorithm. In this paper, we present an approach to language grounding which automatically interprets text in the context of a complex control application, such as a game, and uses domain knowledge extracted from the text to improve control performance. Both text analysis and control strategies are learned jointly using only a feedback signal inherent to the application. To effectively leverage textual information, our method automatically extracts the text segment most relevant to the current game state, and labels it with a task-centric predicate structure. This labeled text is then used to bias an action selection policy for the game, guiding it towards promising regions of the action space. We encode our model for text analysis and game playing in a multi-layer neural network, representing linguistic decisions via latent variables in the hidden layers, and game action quality via the output layer. Operating within the Monte-Carlo Search framework, we estimate model parameters using feedback from simulated games. We apply our approach to the complex strategy game Civilization II using the official game manual as the text guide. Our results show that a linguistically-informed game-playing agent significantly outperforms its language-unaware counterpart, yielding a 34% absolute improvement and winning over 65% of games when playing against the built-in AI of Civilization.

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 "faster shift reduce constituent parsing with a non binary bottom up strategy"):
  with members:
    - "what s going on in neural constituency parsers an analysis"
    - "syntactic scaffolds for semantic structures"
    - "a tree based decoder for neural machine translation"
    - "constituency parsing with a self attentive encoder"
    - "efficient dependency guided named entity recognition"
    - "in order transition based constituent parsing"
    - "a minimal span based neural constituency parser"
    - "towards string to tree neural machine translation"

Connected through hyperedge HE-3 (induced by source paper "language models as knowledge bases"):
  with members:
    - "right for the wrong reasons diagnosing syntactic heuristics in natural language inference"
    - "assessing bert s syntactic abilities"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "transformer xl attentive language models beyond a fixed length context"
    - "non monotonic sequential text generation"
    - "a survey of reinforcement learning informed by natural language"
    - "babyai first steps towards grounded language learning with a human in the loop"
    - "glue a multi task benchmark and analysis platform for natural language understanding"

Connected through hyperedge HE-4 (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-5 (induced by source paper "using natural language for reward shaping in reinforcement learning"):
  with members:
    - "reinforced cross modal matching and self supervised imitation learning for vision language navigation"
    - "learning to understand goal specifications by modelling reward"
    - "accurately and efficiently interpreting human robot instructions of varying granularities"
    - "leveraging demonstrations for deep reinforcement learning on robotics problems with sparse rewards"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "dcn mixed objective and deep residual coattention for question answering"
    - "mapping instructions and visual observations to actions with reinforcement learning"
    - "the atari grand challenge dataset"

Connected through hyperedge HE-6 (induced by source paper "pre learning environment representations for data efficient neural instruction following"):
  with members:
    - "model based reinforcement learning for atari"
    - "the fast and the flexible training neural networks to learn to follow instructions from small data"
    - "mapping instructions to actions in 3d environments with visual goal prediction"
    - "look before you leap bridging model free and model based reinforcement learning for planned ahead vision and language navigation"
    - "analogs of linguistic structure in deep representations"
    - "natural language does not emerge naturally in multi agent dialog"
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "emergence of grounded compositional language in multi agent populations"

Connected through hyperedge HE-7 (induced by source paper "learning to learn semantic parsers from natural language supervision"):
  with members:
    - "guiding reinforcement learning exploration using natural language"
    - "learning a neural semantic parser from user feedback"
    - "program induction by rationale generation learning to solve and explain algebraic word problems"
    - "neural symbolic machines learning semantic parsers on freebase with weak supervision"
    - "data recombination for neural semantic parsing"
    - "effective approaches to attention based neural machine translation"
    - "language understanding for text based games using deep reinforcement learning"
    - "compositional semantic parsing on semi structured tables"

Connected through hyperedge HE-8 (induced by source paper "transflow learning repurposing flow models without retraining"):
  with members:
    - "image2stylegan how to embed images into the stylegan latent space"
    - "few shot unsupervised image to image translation"
    - "flow improving flow based generative models with variational dequantization and architecture design"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "latent constraints learning to generate conditionally from unconditional generative models"
    - "density estimation using real nvp"
    - "conditional image generation with pixelcnn decoders"
    - "adversarial feature 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.CL(Computation and Language)