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: "reaching in clutter with whole arm tactile sensing"
  Abstract: Clutter creates challenges for robot manipulation, including a lack of non-contact trajectories and reduced visibility for line-of-sight sensors. We demonstrate that robots can use whole-arm tactile sensing to perceive clutter and maneuver within it, while keeping contact forces low. We first present our approach to manipulation, which emphasizes the benefits of making contact across the entire manipulator and assumes the manipulator has low-stiffness actuation and tactile sensing across its entire surface. We then present a novel controller that exploits these assumptions. The controller only requires haptic sensing, handles multiple contacts, and does not need an explicit model of the environment prior to contact. It uses model predictive control with a time horizon of length one and a linear quasi-static mechanical model. In our experiments, the controller enabled a real robot and a simulated robot to reach goal locations in a variety of environments, including artificial foliage, a cinder block, and randomly generated clutter, while keeping contact forces low. While reaching, the robots performed maneuvers that included bending objects, compressing objects, sliding objects, and pivoting around objects. In simulation, whole-arm tactile sensing also outperformed per-link force-torque sensing in moderate clutter, with the relative benefits increasing with the amount of clutter.

Connected through hyperedge HE-1 (induced by source paper "learning to collaborate from simulation for robot assisted dressing"):
  with members:
    - "assistive gym a physics simulation framework for assistive robotics"
    - "multidimensional capacitive sensing for robot assisted dressing and bathing"
    - "deepmimic example guided deep reinforcement learning of physics based character skills"
    - "deep haptic model predictive control for robot assisted dressing"
    - "data driven approach to simulating realistic human joint constraints"
    - "tracking human pose during robot assisted dressing using single axis capacitive proximity sensing"
    - "high dimensional continuous control using generalized advantage estimation"
    - "continuous control with deep reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "toward active robot assisted feeding with a general purpose mobile manipulator design evaluation and lessons learned"):
  with members:
    - "in home and remote use of robotic body surrogates by people with profound motor deficits"
    - "shared autonomy via hindsight optimization for teleoperation and teaming"
    - "a multimodal anomaly detector for robot assisted feeding using an lstm based variational autoencoder"
    - "tracking human pose during robot assisted dressing using single axis capacitive proximity sensing"
    - "towards assistive feeding with a general purpose mobile manipulator"

Connected through hyperedge HE-3 (induced by source paper "soft bubble a highly compliant dense geometry tactile sensor for robot manipulation"):
  with members:
    - "gelslim a high resolution compact robust and calibrated tactile sensing finger"
    - "the feeling of success does touch sensing help predict grasp outcomes"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-4 (induced by source paper "deep haptic model predictive control for robot assisted dressing"):
  with members:
    - "deep visual foresight for planning robot motion"
    - "the cma evolution strategy a tutorial"
    - "one shot learning of manipulation skills with online dynamics adaptation and neural network priors"
    - "embed to control a locally linear latent dynamics model for control from raw images"

Connected through hyperedge HE-5 (induced by source paper "a multimodal anomaly detector for robot assisted feeding using an lstm based variational autoencoder"):
  with members:
    - "towards assistive feeding with a general purpose mobile manipulator"
    - "recurrent neural radio anomaly detection"
    - "lstm based encoder decoder for multi sensor anomaly detection"
    - "generating sentences from a continuous space"
    - "denoising criterion for variational auto encoding framework"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

Connected through hyperedge HE-6 (induced by source paper "learning to navigate cloth using haptics"):
  with members:
    - "asynchronous methods for deep reinforcement learning"
    - "q prop sample efficient policy gradient with an off policy critic"
    - "high dimensional continuous control using generalized advantage estimation"
    - "continuous control with deep reinforcement learning"
    - "trust region policy optimization"

Connected through hyperedge HE-7 (induced by source paper "inferring object properties from incidental contact with a tactile sensing forearm"):
  with members:
    - "deep learning for tactile understanding from visual and haptic data"
    - "dropout improves recurrent neural networks for handwriting recognition"
    - "generating sequences with recurrent neural networks"

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)