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: "extracting action sequences from texts based on deep reinforcement learning"
  Abstract: Extracting action sequences from texts in natural language is challenging, which requires commonsense inferences based on world knowledge. Although there has been work on extracting action scripts, instructions, navigation actions, etc., they require either the set of candidate actions is provided in advance, or action descriptions are restricted in a specific form, e.g., description templates. In this paper, we aim to extract action sequences from texts in free natural language, i.e., without any restricted templates, provided the candidate set of actions is unknown. We propose to extract action sequences from texts based on the deep reinforcement learning framework. Specifically, we view "selecting" or "eliminating" words from texts as "actions", and texts associated with actions as "states". We then build Q-networks to learn the policy of extracting actions and extract plans from the labelled texts. We exhibit the effectiveness of our approach in several datasets with comparison to state-of-the-art approaches, including online experiments interacting with humans.

Connected through hyperedge HE-1 (induced by source paper "federated deep reinforcement learning"):
  with members:
    - "federated machine learning concept and applications"
    - "fully convolutional network with multi step reinforcement learning for image processing"
    - "self consistent trajectory autoencoder hierarchical reinforcement learning with trajectory embeddings"
    - "multi agent reinforcement learning in sequential social dilemmas"
    - "deep decentralized multi task multi agent reinforcement learning under partial observability"
    - "federated multi task learning"
    - "communication efficient learning of deep networks from decentralized data"
    - "learning to communicate with deep multi agent reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "neural symbolic descriptive action model from images the search for strips"):
  with members:
    - "unsupervised grounding of plannable first order logic representation from images"
    - "learning action models from disordered and noisy plan traces"
    - "towards stable symbol grounding with zero suppressed state autoencoder"
    - "learning strips action models with classical planning"
    - "learning plannable representations with causal infogan"
    - "learning finite state representations of recurrent policy networks"
    - "dvae discrete variational autoencoders with overlapping transformations"
    - "lstm based goal recognition in latent space"

Connected through hyperedge HE-3 (induced by source paper "transfer value iteration networks"):
  with members:
    - "transfer in deep reinforcement learning using successor features and generalised policy improvement"
    - "gated path planning networks"
    - "value prediction network"
    - "cognitive mapping and planning for visual navigation"
    - "the predictron end to end learning and planning"
    - "value iteration networks"
    - "end to end goal driven web navigation"
    - "actor mimic deep multitask and transfer reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "federated reinforcement learning"):
  with members:
    - "federated machine learning concept and applications"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "fully convolutional network with multi step reinforcement learning for image processing"
    - "self consistent trajectory autoencoder hierarchical reinforcement learning with trajectory embeddings"
    - "multi agent reinforcement learning in sequential social dilemmas"
    - "deep decentralized multi task multi agent reinforcement learning under partial observability"
    - "federated multi task learning"
    - "communication efficient learning of deep networks from decentralized data"

Connected through hyperedge HE-5 (induced by source paper "learning action models from disordered and noisy plan traces"):
  with members:
    - "learning portable representations for high level planning"
    - "towards stable symbol grounding with zero suppressed state autoencoder"
    - "learning strips action models with classical planning"
    - "efficient safe and probably approximately complete learning of action models"
    - "classical planning in deep latent space bridging the subsymbolic symbolic boundary"
    - "distributed representations of sentences and documents"
    - "learning strips operators from noisy and incomplete observations"
    - "new inference rules for max sat"

Connected through hyperedge HE-6 (induced by source paper "playing by the book an interactive game approach for action graph extraction from text"):
  with members:
    - "leveraging knowledge bases in lstms for improving machine reading"
    - "question answering as global reasoning over semantic abstractions"
    - "a survey of domain adaptation for neural machine translation"
    - "neural symbolic vqa disentangling reasoning from vision and language understanding"
    - "textworld a learning environment for text based games"
    - "building dynamic knowledge graphs from text using machine reading comprehension"
    - "deep dyna q integrating planning for task completion dialogue policy learning"
    - "learn what not to learn action elimination with deep reinforcement 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.AI(Artificial Intelligence)