human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "sim to real reinforcement learning for deformable object manipulation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "when deep learning meets data alignment a review on deep registration networks drns")
  Member "deep haptic model predictive control for robot assisted dressing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep imitation learning of sequential fabric smoothing from an algorithmic supervisor")
  Member "learning robust bed making using deep imitation learning with dart":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "model free vision based shaping of deformable plastic materials")
  Member "adam a method for stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "cnn features off the shelf an astounding baseline for recognition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a large scale study of representation learning with the visual task adaptation benchmark")

Question: Please tell me which class the source paper 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)