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: "robot bed making deep transfer learning using depth sensing of deformable fabric"
  Abstract: Bed-making is a common task well-suited for home robots since it is tolerant to error and not time-critical. Bed-making can also be difficult for senior citizens and those with limited mobility due to the bending and reaching movements required. Autonomous bed-making combines multiple challenges in robotics: perception in unstructured environments, deformable object manipulation, transfer learning, and sequential decision making. We formalize the bed-making problem as one of maximizing surface coverage with a blanket, and explore algorithmic approaches that use deep learning on depth images to be invariant to the color and pattern of the blankets. We train two networks: one to identify a corner of the blanket and another to determine when to transition to the other side of the bed. Using the first network, the robot grasps at its estimate of the blanket corner and then pulls it to the appropriate corner of the bed frame. The second network estimates if the robot has sufficiently covered one side and can transition to the other, or if it should attempt another grasp from the same side. We evaluate with two robots, the Toyota HSR and the Fetch, and three blankets. Using 2018 and 654 depth images for training the grasp and transition networks respectively, experiments with a quarter-scale twin bed achieve an average of 91.7% blanket coverage, nearly matching human supervisors with 95.0% coverage. Data is available at this https URL.

Connected through hyperedge HE-1 (induced by source paper "learning to manipulate deformable objects without demonstrations"):
  with members:
    - "deep imitation learning of sequential fabric smoothing policies"
    - "learning robotic manipulation through visual planning and acting"
    - "deep transfer learning of pick points on fabric for robot bed making"
    - "rlpyt a research code base for deep reinforcement learning in pytorch"
    - "deepmind control suite"
    - "model ensemble trust region policy optimization"
    - "learning dexterous in hand manipulation"
    - "learning based feedback controller for deformable object manipulation"

Connected through hyperedge HE-2 (induced by source paper "keto learning keypoint representations for tool manipulation"):
  with members:
    - "kpam keypoint affordances for category level robotic manipulation"
    - "improvisation through physical understanding using novel objects as tools with visual foresight"
    - "6 dof graspnet variational grasp generation for object manipulation"
    - "learning task oriented grasping for tool manipulation from simulated self supervision"
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"
    - "multi task domain adaptation for deep learning of instance grasping from simulation"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "affordancenet an end to end deep learning approach for object affordance detection"

Connected through hyperedge HE-3 (induced by source paper "kpam keypoint affordances for category level robotic manipulation"):
  with members:
    - "normalized object coordinate space for category level 6d object pose and size estimation"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "category level 6d object pose recovery in depth images"
    - "deep object pose estimation for semantic robotic grasping of household objects"
    - "learning dexterous in hand manipulation"
    - "closing the loop for robotic grasping a real time generative grasp synthesis approach"
    - "fast object learning and dual arm coordination for cluttered stowing picking and packing"
    - "filterreg robust and efficient probabilistic point set registration using gaussian filter and twist parameterization"

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)