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 "2 5d image based robotic grasping"):
  Abstract: We consider the problem of robotic grasping using depth + RGB information sampling from a real sensor. we design an encoder-decoder neural network to predict grasp policy in real time. This method can fuse the advantage of depth image and RGB image at the same time and is robust for grasp and observation height.We evaluate our method in a physical robotic system and propose an open-loop algorithm to realize robotic grasp operation. We analyze the result of experiment from multi-perspective and the result shows that our method is competitive with the state-of-the-art in grasp performance, real-time and model size. The video is available in this https URL

Includes members:
  Member "tossingbot learning to throw arbitrary objects with residual physics":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning to walk in the real world with minimal human effort")
  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 "closing the loop for robotic grasping a real time generative grasp synthesis approach":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deceiving image to image translation networks for autonomous driving with adversarial perturbations")
  Member "more than a feeling learning to grasp and regrasp using vision and touch":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "reaching grasping and re grasping learning multimode grasping skills")
  Member "learning synergies between pushing and grasping with self supervised deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "dgcm net dense geometrical correspondence matching network for incremental experience based robotic grasping")
  Member "making sense of vision and touch self supervised learning of multimodal representations for contact rich tasks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning to walk in the real world with minimal human effort")
  Member "grasp pose detection in point clouds":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "dgcm net dense geometrical correspondence matching network for incremental experience based robotic grasping")
  Member "sim2real view invariant visual servoing by recurrent control":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deep drone racing from simulation to reality with domain randomization")

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)