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 "learning to singulate objects using a push proposal network"):
  Abstract: Learning to act in unstructured environments such as cluttered piles of objects poses a substantial challenge for manipulation robots. We present a novel neural network-based approach that separates unknown objects in clutter by selecting favourable push actions. Our network is trained from data collected through autonomous interaction of a PR2 robot with randomly organized tabletop scenes. The model is designed to propose meaningful push actions based on over-segmented RGB-D images. We evaluate our approach by singulating up to 8 unknown objects in clutter. We demonstrate that our method enables the robot to perform the task with a high success rate and a low number of required push actions. Our results based on real-world experiments show that our network is able to generalize to novel objects of various sizes and shapes as well as to arbitrary object configurations. Videos of our experiments can be viewed at http://robotpush.cs.uni-freiburg.de.

Includes members:
  Member "dex net 2 0 deep learning to plan robust grasps with synthetic point clouds and analytic grasp metrics":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning pregrasp manipulation of objects from ungraspable poses")
  Member "metric learning for generalizing spatial relations to new objects":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning object placements for relational instructions by hallucinating scene representations")
  Member "interactive perception leveraging action in perception and perception in action":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "action for better prediction")
  Member "fast model identification via physics engines for data efficient policy search":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "automatic differentiation and continuous sensitivity analysis of rigid body dynamics")
  Member "more than a million ways to be pushed a high fidelity experimental dataset of planar pushing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "let s push things forward a survey on robot pushing")
  Member "comparing human centric and robot centric sampling for robot deep learning from demonstrations":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "imitation learning as f divergence minimization")
  Member "deep visual foresight for planning robot motion":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "transformation based adversarial video prediction on large scale data")
  Member "high precision grasp pose detection in dense clutter":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "regnet region based grasp network for single shot grasp detection in point clouds")

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)