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: "deep haptic model predictive control for robot assisted dressing"
  Abstract: Robot-assisted dressing offers an opportunity to benefit the lives of many people with disabilities, such as some older adults. However, robots currently lack common sense about the physical implications of their actions on people. The physical implications of dressing are complicated by non-rigid garments, which can result in a robot indirectly applying high forces to a person's body. We present a deep recurrent model that, when given a proposed action by the robot, predicts the forces a garment will apply to a person's body. We also show that a robot can provide better dressing assistance by using this model with model predictive control. The predictions made by our model only use haptic and kinematic observations from the robot's end effector, which are readily attainable. Collecting training data from real world physical human-robot interaction can be time consuming, costly, and put people at risk. Instead, we train our predictive model using data collected in an entirely self-supervised fashion from a physics-based simulation. We evaluated our approach with a PR2 robot that attempted to pull a hospital gown onto the arms of 10 human participants. With a 0.2s prediction horizon, our controller succeeded at high rates and lowered applied force while navigating the garment around a persons fist and elbow without getting caught. Shorter prediction horizons resulted in significantly reduced performance with the sleeve catching on the participants' fists and elbows, demonstrating the value of our model's predictions. These behaviors of mitigating catches emerged from our deep predictive model and the controller objective function, which primarily penalizes high forces.

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"
    - "fast and reliable autonomous surgical debridement with cable driven robots using a two phase calibration procedure"

Connected through hyperedge HE-2 (induced by source paper "a survey of behavior learning applications in robotics state of the art and perspectives"):
  with members:
    - "learning dexterous in hand manipulation"
    - "deepmimic example guided deep reinforcement learning of physics based character skills"
    - "one shot visual imitation learning via meta learning"
    - "deep reinforcement learning a brief survey"
    - "learning complex dexterous manipulation with deep reinforcement learning and demonstrations"
    - "combining model based and model free updates for trajectory centric reinforcement learning"
    - "time contrastive networks self supervised learning from video"
    - "robot composite learning and the nunchaku flipping challenge"

Connected through hyperedge HE-3 (induced by source paper "vid2param modelling of dynamics parameters from video"):
  with members:
    - "bounce and learn modeling scene dynamics with real world bounces"
    - "bayessim adaptive domain randomization via probabilistic inference for robotics simulators"
    - "physics as inverse graphics joint unsupervised learning of objects and physics from video"
    - "using causal analysis to learn specifications from task demonstrations"
    - "tossingbot learning to throw arbitrary objects with residual physics"
    - "active localization of gas leaks using fluid simulation"
    - "interpretable latent spaces for learning from demonstration"
    - "sim to real transfer of robotic control with dynamics randomization"

Connected through hyperedge HE-4 (induced by source paper "assistive gym a physics simulation framework for assistive robotics"):
  with members:
    - "multidimensional capacitive sensing for robot assisted dressing and bathing"
    - "modeling collaboration for robot assisted dressing tasks"
    - "tossingbot learning to throw arbitrary objects with residual physics"
    - "habitat a platform for embodied ai research"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "sim to real learning agile locomotion for quadruped robots"
    - "multi goal reinforcement learning challenging robotics environments and request for research"
    - "task centric optimization of configurations for assistive robots"

Connected through hyperedge HE-5 (induced by source paper "learning to guide guidance law based on deep meta learning and model predictive path integral control"):
  with members:
    - "adaptive guidance with reinforcement meta learning"
    - "learning to adapt in dynamic real world environments through meta reinforcement learning"
    - "learning to learn how to learn self adaptive visual navigation using meta learning"
    - "learning image conditioned dynamics models for control of under actuated legged millirobots"
    - "data efficient reinforcement learning with probabilistic model predictive control"
    - "neural network dynamics for model based deep reinforcement learning with model free fine tuning"
    - "continuous adaptation via meta learning in nonstationary and competitive environments"
    - "control of a quadrotor with reinforcement learning"

Connected through hyperedge HE-6 (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"
    - "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"
    - "trust region policy optimization"

Connected through hyperedge HE-7 (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"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "tracking human pose during robot assisted dressing using single axis capacitive proximity sensing"
    - "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-8 (induced by source paper "data driven model predictive control for food cutting"):
  with members:
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "deep learning and model predictive control for self tuning mode locked lasers"
    - "neural network dynamics for model based deep reinforcement learning with model free fine tuning"
    - "end to end training of deep visuomotor policies"

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)