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: "human robot mutual adaptation in shared autonomy"
  Abstract: Shared autonomy integrates user input with robot autonomy in order to control a robot and help the user to complete a task. Our work aims to improve the performance of such a human-robot team: the robot tries to guide the human towards an effective strategy, sometimes against the human's own preference, while still retaining his trust. We achieve this through a principled human-robot mutual adaptation formalism. We integrate a bounded-memory adaptation model of the human into a partially observable stochastic decision model, which enables the robot to adapt to an adaptable human. When the human is adaptable, the robot guides the human towards a good strategy, maybe unknown to the human in advance. When the human is stubborn and not adaptable, the robot complies with the human's preference in order to retain their trust. In the shared autonomy setting, unlike many other common human-robot collaboration settings, only the robot actions can change the physical state of the world, and the human and robot goals are not fully observable. We address these challenges and show in a human subject experiment that the proposed mutual adaptation formalism improves human-robot team performance, while retaining a high level of user trust in the robot, compared to the common approach of having the robot strictly following participants' preference.

Connected through hyperedge HE-1 (induced by source paper "a general arbitration model for robust human robot shared control with multi source uncertainty modeling"):
  with members:
    - "shared autonomy via deep reinforcement learning"
    - "shared autonomy via hindsight optimization for teleoperation and teaming"
    - "uncertainty based arbitration of human machine shared control"
    - "autonomy infused teleoperation with application to bci manipulation"
    - "shared autonomy via hindsight optimization"

Connected through hyperedge HE-2 (induced by source paper "learn task first or learn human partner first deep reinforcement learning of human robot cooperation in asymmetric hierarchical dynamic task"):
  with members:
    - "shared autonomy via deep reinforcement learning"
    - "shared autonomy via hindsight optimization"
    - "reinforcement learning a survey"

Connected through hyperedge HE-3 (induced by source paper "learn and transfer knowledge of preferred assistance strategies in semi autonomous telemanipulation"):
  with members:
    - "vision based teleoperation of shadow dexterous hand using end to end deep neural network"
    - "the feeling of success does touch sensing help predict grasp outcomes"

Connected through hyperedge HE-4 (induced by source paper "multi task trust transfer for human robot interaction"):
  with members:
    - "robot capability and intention in trust based decisions across tasks"
    - "establishing appropriate trust via critical states"
    - "deep reinforcement learning based image captioning with embedding reward"
    - "game theoretic modeling of human adaptation in human robot collaboration"
    - "generating sentences from a continuous space"
    - "adam a method for stochastic optimization"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-5 (induced by source paper "learning arbitration for shared autonomy by hindsight data aggregation"):
  with members:
    - "a blended human robot shared control framework to handle drift and latency"
    - "shared autonomy via deep reinforcement learning"
    - "shared autonomy via hindsight optimization for teleoperation and teaming"
    - "real time perception meets reactive motion generation"
    - "goal set inverse optimal control and iterative re planning for predicting human reaching motions in shared workspaces"

Connected through hyperedge HE-6 (induced by source paper "enabling robots to infer how end users teach and learn through human robot interaction"):
  with members:
    - "an algorithmic perspective on imitation learning"
    - "enabling robots to communicate their objectives"
    - "machine teaching for bayesian learners in the exponential family"

Connected through hyperedge HE-7 (induced by source paper "shared autonomy via deep reinforcement learning"):
  with members:
    - "learning models for shared control of human machine systems with unknown dynamics"
    - "rainbow combining improvements in deep reinforcement learning"
    - "explore exploit or listen combining human feedback and policy model to speed up deep reinforcement learning in 3d worlds"
    - "deep tamer interactive agent shaping in high dimensional state spaces"
    - "comparing human centric and robot centric sampling for robot deep learning from demonstrations"
    - "learning language games through interaction"
    - "deep recurrent q learning for partially observable mdps"
    - "deep reinforcement learning with double q learning"

Connected through hyperedge HE-8 (induced by source paper "the transfer of human trust in robot capabilities across tasks"):
  with members:
    - "establishing appropriate trust via critical states"
    - "adam a method for stochastic optimization"
    - "efficient estimation of word representations in vector space"

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)