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 "quickly inserting pegs into uncertain holes using multi view images and deep network trained on synthetic data"):
  Abstract: This paper uses robots to assemble pegs into holes on surfaces with different colors and textures. It especially targets at the problem of peg-in-hole assembly with initial position uncertainty. Two in-hand cameras and a force-torque sensor are used to account for the position uncertainty. A program sequence comprising learning-based visual servoing, spiral search, and impedance control is implemented to perform the peg-in-hole task with feedback from the above sensors. Contributions are mainly made in the learning-based visual servoing of the sequence, where a deep neural network is trained with various sets of synthetic data generated using the concept of domain randomization to predict where a hole is. In the experiments and analysis section, the network is analyzed and compared, and a real-world robotic system to insert pegs to holes using the proposed method is implemented. The results show that the implemented peg-in-hole assembly system can perform successful peg-in-hole insertions on surfaces with various colors and textures. It can generally speed up the entire peg-in-hole process.

Includes members:
  Member "implicit 3d orientation learning for 6d object detection from rgb images":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "cps class level 6d pose and shape estimation from monocular images")
  Member "a probabilistic framework for tracking uncertainties in robotic manipulation":
    also connected through other hyperedges:
      (none)
  Member "learning robotic assembly from cad":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning precise 3d manipulation from multiple uncalibrated cameras")
  Member "learning dexterous in hand manipulation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "never give up learning directed exploration strategies")
  Member "deep learning scooping motion using bilateral teleoperations":
    also connected through other hyperedges:
      (none)
  Member "making sense of vision and touch self supervised learning of multimodal representations for contact rich tasks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning to walk in the real world with minimal human effort")
  Member "experimental force torque dataset for robot learning of multi shape insertion":
    also connected through other hyperedges:
      (none)
  Member "structured domain randomization bridging the reality gap by context aware synthetic data":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "increasing generality in machine learning through procedural content generation")

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)