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: "crossmodal attentive skill learner"
  Abstract: This paper presents the Crossmodal Attentive Skill Learner (CASL), integrated with the recently-introduced Asynchronous Advantage Option-Critic (A2OC) architecture [Harb et al., 2017] to enable hierarchical reinforcement learning across multiple sensory inputs. We provide concrete examples where the approach not only improves performance in a single task, but accelerates transfer to new tasks. We demonstrate the attention mechanism anticipates and identifies useful latent features, while filtering irrelevant sensor modalities during execution. We modify the Arcade Learning Environment [Bellemare et al., 2013] to support audio queries, and conduct evaluations of crossmodal learning in the Atari 2600 game Amidar. Finally, building on the recent work of Babaeizadeh et al. [2017], we open-source a fast hybrid CPU-GPU implementation of CASL.

Connected through hyperedge HE-1 (induced by source paper "collision avoidance in pedestrian rich environments with deep reinforcement learning"):
  with members:
    - "addressing function approximation error in actor critic methods"
    - "socially aware motion planning with deep reinforcement learning"
    - "virtual to real deep reinforcement learning continuous control of mobile robots for mapless navigation"
    - "proximal policy optimization algorithms"
    - "decentralized non communicating multiagent collision avoidance with deep reinforcement learning"
    - "dynamic arrival rate estimation for campus mobility on demand network graphs"
    - "tensorflow a system for large scale machine learning"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "motion planning among dynamic decision making agents with deep reinforcement learning"):
  with members:
    - "intent aware multi agent reinforcement learning"
    - "towards optimally decentralized multi robot collision avoidance via deep reinforcement learning"
    - "socially aware motion planning with deep reinforcement learning"
    - "role playing learning for socially concomitant mobile robot navigation"
    - "virtual to real deep reinforcement learning continuous control of mobile robots for mapless navigation"
    - "ssd single shot multibox detector"
    - "dynamic arrival rate estimation for campus mobility on demand network graphs"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-3 (induced by source paper "escape room a configurable testbed for hierarchical reinforcement learning"):
  with members:
    - "rainbow combining improvements in deep reinforcement learning"
    - "feudal networks for hierarchical reinforcement learning"
    - "when waiting is not an option learning options with a deliberation cost"
    - "hierarchical policy search via return weighted density estimation"
    - "deep abstract q networks"
    - "the option critic architecture"
    - "the arcade learning environment an evaluation platform for general agents"
    - "playing atari 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)