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: "show attend and interact perceivable human robot social interaction through neural attention q network"
  Abstract: For a safe, natural and effective human-robot social interaction, it is essential to develop a system that allows a robot to demonstrate the perceivable responsive behaviors to complex human behaviors. We introduce the Multimodal Deep Attention Recurrent Q-Network using which the robot exhibits human-like social interaction skills after 14 days of interacting with people in an uncontrolled real world. Each and every day during the 14 days, the system gathered robot interaction experiences with people through a hit-and-trial method and then trained the MDARQN on these experiences using end-to-end reinforcement learning approach. The results of interaction based learning indicate that the robot has learned to respond to complex human behaviors in a perceivable and socially acceptable manner.

Connected through hyperedge HE-1 (induced by source paper "diverse and admissible trajectory forecasting through multimodal context understanding"):
  with members:
    - "human motion trajectory prediction a survey"
    - "stochastic prediction of multi agent interactions from partial observations"
    - "multi agent tensor fusion for contextual trajectory prediction"
    - "precog prediction conditioned on goals in visual multi agent settings"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "convolutional social pooling for vehicle trajectory prediction"
    - "social gan socially acceptable trajectories with generative adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "composing ensembles of policies with deep reinforcement learning"):
  with members:
    - "intrinsically motivated reinforcement learning for human robot interaction in the real world"
    - "data efficient hierarchical reinforcement learning"
    - "composable deep reinforcement learning for robotic manipulation"
    - "addressing function approximation error in actor critic methods"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "learning to compose skills"
    - "feudal networks for hierarchical reinforcement learning"
    - "when waiting is not an option learning options with a deliberation cost"

Connected through hyperedge HE-3 (induced by source paper "composing task agnostic policies with deep reinforcement learning"):
  with members:
    - "mcp learning composable hierarchical control with multiplicative compositional policies"
    - "intrinsically motivated reinforcement learning for human robot interaction in the real world"
    - "composable deep reinforcement learning for robotic manipulation"
    - "addressing function approximation error in actor critic methods"
    - "learning to compose skills"
    - "feudal networks for hierarchical reinforcement learning"
    - "when waiting is not an option learning options with a deliberation cost"
    - "stochastic neural networks for hierarchical reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "a data efficient deep learning approach for deployable multimodal social robots"):
  with members:
    - "deep reinforcement learning for chatbots using clustered actions and human likeness rewards"
    - "robot gains social intelligence through multimodal deep reinforcement learning"
    - "simpleds a simple deep reinforcement learning dialogue system"
    - "learning to play in a day faster deep reinforcement learning by optimality tightening"
    - "asynchronous methods for deep reinforcement learning"
    - "training an interactive humanoid robot using multimodal deep reinforcement learning"
    - "hierarchical deep reinforcement learning integrating temporal abstraction and intrinsic motivation"
    - "prioritized experience replay"

Connected through hyperedge HE-5 (induced by source paper "neural network based reinforcement learning for audio visual gaze control in human robot interaction"):
  with members:
    - "audio visual speaker diarization based on spatiotemporal bayesian fusion"
    - "robot gains social intelligence through multimodal deep reinforcement learning"
    - "multiple speaker localization based on direct path features and likelihood maximization with spatial sparsity regularization"
    - "a sensorimotor reinforcement learning framework for physical human robot interaction"
    - "realtime multi person 2d pose estimation using part affinity fields"
    - "adam a method for stochastic optimization"
    - "playing atari with deep reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "speech driven backchannel generation using deep q network for enhancing engagement in human robot interaction"):
  with members:
    - "neural network based reinforcement learning for audio visual gaze control in human robot interaction"
    - "behaviour policy estimation in off policy policy evaluation calibration matters"
    - "robot gains social intelligence through multimodal deep reinforcement learning"
    - "deep q learning from demonstrations"

Connected through hyperedge HE-7 (induced by source paper "batch recurrent q learning for backchannel generation towards engaging agents"):
  with members:
    - "neural network based reinforcement learning for audio visual gaze control in human robot interaction"
    - "behaviour policy estimation in off policy policy evaluation calibration matters"
    - "data efficient off policy policy evaluation for reinforcement learning"
    - "deep recurrent q learning for partially observable mdps"

Connected through hyperedge HE-8 (induced by source paper "adversarial imitation via variational inverse reinforcement learning"):
  with members:
    - "intrinsically motivated reinforcement learning for human robot interaction in the real world"
    - "learning robust rewards with adversarial inverse reinforcement learning"
    - "robot autonomy for surgery"
    - "proximal policy optimization algorithms"
    - "generative adversarial imitation learning"
    - "a connection between generative adversarial networks inverse reinforcement learning and energy based models"
    - "guided cost learning deep inverse optimal control via policy optimization"
    - "trust region policy 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.RO(Robotics)