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: "tracking human pose during robot assisted dressing using single axis capacitive proximity sensing"
  Abstract: Dressing is a fundamental task of everyday living and robots offer an opportunity to assist people with motor impairments. While several robotic systems have explored robot-assisted dressing, few have considered how a robot can manage errors in human pose estimation, or adapt to human motion in real time during dressing assistance. In addition, estimating pose changes due to human motion can be challenging with vision-based techniques since dressing is often intended to visually occlude the body with clothing. We present a method to track a person's pose in real time using capacitive proximity sensing. This sensing approach gives direct estimates of distance with low latency, has a high signal-to-noise ratio, and has low computational requirements. Using our method, a robot can adjust for errors in the estimated pose of a person and physically follow the contours and movements of the person while providing dressing assistance. As part of an evaluation of our method, the robot successfully pulled the sleeve of a hospital gown and a cardigan onto the right arms of 10 human participants, despite arm motions and large errors in the initially estimated pose of the person's arm. We also show that a capacitive sensor is unaffected by visual occlusion of the body and can sense a person's body through fabric clothing.

Connected through hyperedge HE-1 (induced by source paper "deep imitation learning of sequential fabric smoothing from an algorithmic supervisor"):
  with members:
    - "a grasping centered analysis for cloth manipulation"
    - "model based behavioral cloning with future image similarity learning"
    - "learning to manipulate deformable objects without demonstrations"
    - "dynamic cloth manipulation with deep reinforcement learning"
    - "deep transfer learning of pick points on fabric for robot bed making"
    - "sim to real reinforcement learning for deformable object manipulation"
    - "visual foresight model based deep reinforcement learning for vision based robotic control"
    - "deep haptic model predictive control for robot assisted dressing"

Connected through hyperedge HE-2 (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"
    - "high dimensional continuous control using generalized advantage estimation"
    - "continuous control with deep reinforcement learning"
    - "trust region policy optimization"

Connected through hyperedge HE-3 (induced by source paper "deep imitation learning of sequential fabric smoothing policies"):
  with members:
    - "a grasping centered analysis for cloth manipulation"
    - "learning dexterous in hand manipulation"
    - "visual foresight model based deep reinforcement learning for vision based robotic control"
    - "deep haptic model predictive control for robot assisted dressing"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "cad2rl real single image flight without a single real image"
    - "folding deformable objects using predictive simulation and trajectory optimization"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (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"
    - "towards assistive feeding with a general purpose mobile manipulator"
    - "reaching in clutter with whole arm tactile sensing"

Connected through hyperedge HE-5 (induced by source paper "multidimensional capacitive sensing for robot assisted dressing and bathing"):
  with members:
    - "deep haptic model predictive control for robot assisted dressing"
    - "3d human pose estimation on a configurable bed from a pressure image"
    - "multimodal signal processing and learning aspects of human robot interaction for an assistive bathing robot"

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)