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 "inferring 3d shapes of unknown rigid objects in clutter through inverse physics reasoning"):
  Abstract: We present a probabilistic approach for building, on the fly, three dimensional (3D) models of unknown objects while being manipulated by a robot. We specifically consider manipulation tasks in piles of clutter that contain previously unseen objects. Most manipulation algorithms for performing such tasks require known geometric models of the objects in order to grasp or rearrange them robustly. One of the novel aspects of this work is the utilization of a physics engine for verifying hypothesized geometries in simulation. The evidence provided by physics simulations is used in a probabilistic framework that accounts for the fact that mechanical properties of the objects are uncertain. We present an efficient algorithm for inferring occluded parts of objects based on their observed motions and mutual interactions. Experiments using a robot show that this approach is efficient for constructing physically realistic 3D models, which can be useful for manipulation planning. Experiments also show that the proposed approach significantly outperforms alternative approaches in terms of shape accuracy.

Includes members:
  Member "fusion volumetric object level slam":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "3d dynamic scene graphs actionable spatial perception with places objects and humans")
  Member "interactive perception leveraging action in perception and perception in action":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "action for better prediction")
  Member "exploiting symmetry and or manhattan properties for 3d object structure estimation from single and multiple images":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "exploring partial intrinsic and extrinsic symmetry in 3d medical imaging")
  Member "shape completion enabled robotic grasping":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning precise 3d manipulation from multiple uncalibrated cameras")
  Member "analysis and observations from the first amazon picking challenge":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "robust 6d object pose estimation by learning rgb d features")
  Member "benchmarking in manipulation research using the yale cmu berkeley object and model set":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "6d object pose regression via supervised learning on point clouds")
  Member "a dataset for improved rgbd based object detection and pose estimation for warehouse pick and place":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "template based unseen instance detection")
  Member "deep multimodal embedding manipulating novel objects with point clouds language and trajectories":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "urban2vec incorporating street view imagery and pois for multi modal urban neighborhood embedding")

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)