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: "sample efficient deep reinforcement learning for dialogue systems with large action spaces"
  Abstract: In spoken dialogue systems, we aim to deploy artificial intelligence to build automated dialogue agents that can converse with humans. A part of this effort is the policy optimisation task, which attempts to find a policy describing how to respond to humans, in the form of a function taking the current state of the dialogue and returning the response of the system. In this paper, we investigate deep reinforcement learning approaches to solve this problem. Particular attention is given to actor-critic methods, off-policy reinforcement learning with experience replay, and various methods aimed at reducing the bias and variance of estimators. When combined, these methods result in the previously proposed ACER algorithm that gave competitive results in gaming environments. These environments however are fully observable and have a relatively small action set so in this paper we examine the application of ACER to dialogue policy optimisation. We show that this method beats the current state-of-the-art in deep learning approaches for spoken dialogue systems. This not only leads to a more sample efficient algorithm that can train faster, but also allows us to apply the algorithm in more difficult environments than before. We thus experiment with learning in a very large action space, which has two orders of magnitude more actions than previously considered. We find that ACER trains significantly faster than the current state-of-the-art.

Connected through hyperedge HE-1 (induced by source paper "follow the attention combining partial pose and object motion for fine grained action detection"):
  with members:
    - "skeleton based action recognition of people handling objects"
    - "an empirical study of spatial attention mechanisms in deep networks"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "simple baselines for human pose estimation and tracking"
    - "2d 3d pose estimation and action recognition using multitask deep learning"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "rethinking the faster r cnn architecture for temporal action localization"
    - "densepose dense human pose estimation in the wild"

Connected through hyperedge HE-2 (induced by source paper "agentgraph towards universal dialogue management with structured deep reinforcement learning"):
  with members:
    - "towards universal dialogue state tracking"
    - "feudal reinforcement learning for dialogue management in large domains"
    - "large scale multi domain belief tracking with knowledge sharing"
    - "rainbow combining improvements in deep reinforcement learning"
    - "non local neural networks"
    - "sample efficient actor critic reinforcement learning with supervised data for dialogue management"
    - "composite task completion dialogue policy learning via hierarchical deep reinforcement learning"
    - "composite task completion dialogue policy learning via hierarchical deep reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "did you hear that learning to play video games from audio cues"):
  with members:
    - "general video game ai a multi track framework for evaluating agents games and content generation algorithms"
    - "end to end learning for self driving cars"

Connected through hyperedge HE-4 (induced by source paper "efficient dialog policy learning via positive memory retention"):
  with members:
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "learning goal oriented visual dialog via tempered policy gradient"
    - "evaluating visual conversational agents via cooperative human ai games"
    - "sample efficient actor critic reinforcement learning with supervised data for dialogue management"
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "end to end task completion neural dialogue systems"
    - "visual reference resolution using attention memory for visual dialog"
    - "deal or no deal end to end learning for negotiation dialogues"

Connected through hyperedge HE-5 (induced by source paper "neural user simulation for corpus based policy optimisation for spoken dialogue systems"):
  with members:
    - "building a conversational agent overnight with dialogue self play"
    - "large scale multi domain belief tracking with knowledge sharing"
    - "iterative policy learning in end to end trainable task oriented neural dialog models"
    - "a sequence to sequence model for user simulation in spoken dialogue systems"
    - "neural belief tracker data driven dialogue state tracking"
    - "recurrent polynomial network for dialogue state tracking"
    - "incremental lstm based dialog state tracker"
    - "adam a method for stochastic optimization"

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)