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: "neural network based reinforcement learning for audio visual gaze control in human robot interaction"
  Abstract: This paper introduces a novel neural network-based reinforcement learning approach for robot gaze control. Our approach enables a robot to learn and adapt its gaze control strategy for human-robot interaction without the use of external sensors or human supervision. The robot learns to focus its attention on groups of people from its own audio-visual experiences, and independently of the number of people in the environment, their position and physical appearance. In particular, we use recurrent neural networks and Q-learning to find an optimal action-selection policy, and we pretrain on a synthetic environment that simulates sound sources and moving participants to avoid the need of interacting with people for hours. Our experimental evaluation suggests that the proposed method is robust in terms of parameters configuration (i.e. the selection of the parameter values has not a decisive impact on the performance). The best results are obtained when audio and video information are jointly used, and when a late fusion strategy is employed (i.e. when both sources of information are separately processed and then fused). Successful experiments on a real environment with the Nao robot indicate that our framework is a step forward towards the autonomous learning of a perceivable and socially acceptable gaze behavior.

Connected through hyperedge HE-1 (induced by source paper "extended gaze following detecting objects in videos beyond the camera field of view"):
  with members:
    - "deep visual attention prediction"
    - "connecting gaze scene and attention generalized attention estimation via joint modeling of gaze and scene saliency"
    - "tracking gaze and visual focus of attention of people involved in social interaction"
    - "eye tracking for everyone"
    - "an on line variational bayesian model for multi person tracking from cluttered scenes"
    - "u net convolutional networks for biomedical image segmentation"
    - "appearance based gaze estimation in the wild"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"

Connected through hyperedge HE-2 (induced by source paper "exploring temporal dependencies in multimodal referring expressions with mixed reality"):
  with members:
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a comparison of visualisation methods for disambiguating verbal requests in human robot interaction"
    - "action anticipation reading the intentions of humans and robots"
    - "robot gains social intelligence through multimodal deep reinforcement learning"
    - "translating videos to natural language using deep recurrent neural networks"

Connected through hyperedge HE-3 (induced by source paper "speech driven backchannel generation using deep q network for enhancing engagement in human robot interaction"):
  with members:
    - "behaviour policy estimation in off policy policy evaluation calibration matters"
    - "robot gains social intelligence through multimodal deep reinforcement learning"
    - "show attend and interact perceivable human robot social interaction through neural attention q network"
    - "deep q learning from demonstrations"

Connected through hyperedge HE-4 (induced by source paper "batch recurrent q learning for backchannel generation towards engaging agents"):
  with members:
    - "behaviour policy estimation in off policy policy evaluation calibration matters"
    - "show attend and interact perceivable human robot social interaction through neural attention q network"
    - "data efficient off policy policy evaluation for reinforcement learning"
    - "deep recurrent q learning for partially observable mdps"

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.RO(Robotics)