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: "grounding language for transfer in deep reinforcement learning"
  Abstract: In this paper, we explore the utilization of natural language to drive transfer for reinforcement learning (RL). Despite the wide-spread application of deep RL techniques, learning generalized policy representations that work across domains remains a challenging problem. We demonstrate that textual descriptions of environments provide a compact intermediate channel to facilitate effective policy transfer. Specifically, by learning to ground the meaning of text to the dynamics of the environment such as transitions and rewards, an autonomous agent can effectively bootstrap policy learning on a new domain given its description. We employ a model-based RL approach consisting of a differentiable planning module, a model-free component and a factorized state representation to effectively use entity descriptions. Our model outperforms prior work on both transfer and multi-task scenarios in a variety of different environments. For instance, we achieve up to 14% and 11.5% absolute improvement over previously existing models in terms of average and initial rewards, respectively.

Connected through hyperedge HE-1 (induced by source paper "entity abstraction in visual model based reinforcement learning"):
  with members:
    - "combining physical simulators and object based networks for control"
    - "monet unsupervised scene decomposition and representation"
    - "task agnostic dynamics priors for deep reinforcement learning"
    - "unsupervised learning of object keypoints for perception and control"
    - "object oriented dynamics learning through multi level abstraction"
    - "cobra data efficient model based rl through unsupervised object discovery and curiosity driven exploration"
    - "deep set prediction networks"
    - "unsupervised discovery of parts structure and dynamics"

Connected through hyperedge HE-2 (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-3 (induced by source paper "fast task adaptation for tasks labeled using natural language in reinforcement learning"):
  with members:
    - "language as an abstraction for hierarchical deep reinforcement learning"
    - "using natural language for reward shaping in reinforcement learning"
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "a survey of reinforcement learning informed by natural language"
    - "babyai first steps towards grounded language learning with a human in the loop"
    - "zero shot task generalization with multi task deep reinforcement learning"
    - "mapping instructions and visual observations to actions with reinforcement learning"
    - "grounded language learning in a simulated 3d world"

Connected through hyperedge HE-4 (induced by source paper "building generalizable agents with a realistic and rich 3d environment"):
  with members:
    - "minos multimodal indoor simulator for navigation in complex environments"
    - "elf an extensive lightweight and flexible research platform for real time strategy games"
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "starcraft ii a new challenge for reinforcement learning"
    - "gated attention architectures for task oriented language grounding"
    - "neural map structured memory for deep reinforcement learning"
    - "parlai a dialog research software platform"

Connected through hyperedge HE-5 (induced by source paper "learning to play general video games via an object embedding network"):
  with members:
    - "investigating human priors for playing video games"
    - "deep sets"
    - "general video game ai a multi track framework for evaluating agents games and content generation algorithms"
    - "visual interaction networks"
    - "a simple neural network module for relational reasoning"
    - "hybrid reward architecture for reinforcement learning"
    - "neural episodic control"
    - "general video game ai learning from screen capture"

Connected through hyperedge HE-6 (induced by source paper "internet congestion control via deep reinforcement learning"):
  with members:
    - "efficient gan based anomaly detection"
    - "iroko a framework to prototype reinforcement learning for data center traffic control"
    - "rl 2 fast reinforcement learning via slow reinforcement learning"
    - "distance based confidence score for neural network classifiers"
    - "proximal policy optimization algorithms"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "successor features for transfer in reinforcement learning"
    - "value iteration networks"

Connected through hyperedge HE-7 (induced by source paper "investigating human priors for playing video games"):
  with members:
    - "value prediction network"
    - "schema networks zero shot transfer with a generative causal model of intuitive physics"
    - "curiosity driven exploration by self supervised prediction"
    - "asynchronous methods for deep reinforcement learning"
    - "continuous deep q learning with model based acceleration"
    - "building machines that learn and think like people"
    - "unifying count based exploration and intrinsic motivation"

Connected through hyperedge HE-8 (induced by source paper "learning with latent language"):
  with members:
    - "robustfill neural program learning under noisy i o"
    - "representation learning for grounded spatial reasoning"
    - "prototypical networks for few shot learning"
    - "guiding reinforcement learning exploration using natural language"
    - "rl 2 fast reinforcement learning via slow reinforcement learning"
    - "shapeworld a new test methodology for multimodal language understanding"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "program induction by rationale generation learning to solve and explain algebraic word problems"

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)