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 "deep imitation learning of sequential fabric smoothing policies"):
  Abstract: Sequential pulling policies to flatten and smooth fabrics have applications from surgery to manufacturing to home tasks such as bed making and folding clothes. Due to the complexity of fabric states and dynamics, we apply deep imitation learning to learn policies that, given color or depth images of a rectangular fabric sample, estimate pick points and pull vectors to spread the fabric to maximize coverage. To generate data, we develop a fabric simulator and an algorithmic demonstrator that has access to complete state information. We train policies in simulation using domain randomization and dataset aggregation (DAgger) on three tiers of difficulty in the initial randomized configuration. We present results comparing five baseline policies to learned policies and report systematic comparisons of color vs. depth images as inputs. In simulation, learned policies achieve comparable or superior performance to analytic baselines. In 120 physical experiments with the da Vinci Research Kit (dVRK) surgical robot, policies trained in simulation attain 86% and 69% final coverage for color and depth inputs, respectively, suggesting the feasibility of learning fabric smoothing policies from simulation. Supplementary material is available at this https URL fabric-smoothing.

Includes members:
  Member "a grasping centered analysis for cloth manipulation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep imitation learning of sequential fabric smoothing from an algorithmic supervisor")
  Member "learning dexterous in hand manipulation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "never give up learning directed exploration strategies")
  Member "visual foresight model based deep reinforcement learning for vision based robotic control":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "probabilistic future prediction for video scene understanding")
  Member "deep haptic model predictive control for robot assisted dressing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep imitation learning of sequential fabric smoothing from an algorithmic supervisor")
  Member "domain randomization for transferring deep neural networks from simulation to the real world":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "tracking human pose during robot assisted dressing using single axis capacitive proximity sensing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep imitation learning of sequential fabric smoothing from an algorithmic supervisor")
  Member "cad2rl real single image flight without a single real image":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "increasing generality in machine learning through procedural content generation")
  Member "folding deformable objects using predictive simulation and trajectory optimization":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deep imitation learning of sequential fabric smoothing from an algorithmic supervisor")

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)