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 "object finding in cluttered scenes using interactive perception"):
  Abstract: Object finding in clutter is a skill that requires both perception of the environment and in many cases physical interaction. In robotics, interactive perception defines a set of algorithms that leverage actions to improve the perception of the environment, and vice versa use perception to guide the next action. Scene interactions are difficult to model, therefore, most of the current systems use predefined heuristics. This limits their ability to efficiently search for the target object in a complex environment. In order to remove heuristics and the need for explicit models of the interactions, in this work we propose a reinforcement learning based active and interactive perception system for scene exploration and object search. We evaluate our work both in simulated and in real world experiments using a robotic manipulator equipped with an RGB and a depth camera, and compared our system to two baselines. The results indicate that our approach, trained in simulation only, transfers smoothly to reality and can solve the object finding task efficiently and with more than 90% success rate.

Includes members:
  Member "mechanical search multi step retrieval of a target object occluded by clutter":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "toward sim to real directional semantic grasping")
  Member "learning 6 dof grasping and pick place using attention focus":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning manipulation skills via hierarchical spatial attention")
  Member "open3d a modern library for 3d data processing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "neural mesh refiner for 6 dof pose estimation")
  Member "mid level visual representations improve generalization and sample efficiency for learning visuomotor policies":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "scaling and benchmarking self supervised visual representation learning")
  Member "reinforcement learning of active vision for manipulating objects under occlusions":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning precise 3d manipulation from multiple uncalibrated cameras")
  Member "interactive perception leveraging action in perception and perception in action":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "action for better prediction")
  Member "proximal policy optimization algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep reinforcement learning for autonomous driving a survey")
  Member "revisiting active perception":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "action for better prediction")

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)