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 continuous 3d reconstructions for geometrically aware grasping"):
  Abstract: Deep learning has enabled remarkable improvements in grasp synthesis for previously unseen objects viewed from partial views. However, existing approaches lack the ability to explicitly reason about the full 3D geometry of the object when selecting a grasp, relying on indirect geometric reasoning derived when learning grasp success networks. This abandons common sense geometric reasoning, such as avoiding undesired robot object collisions. We propose to utilize a novel, learned 3D reconstruction to enable geometric awareness in a grasping system. We leverage the structure of the reconstruction network to learn a grasp success classifier which serves as the objective function for a continuous grasp optimization. We additionally explicitly constrain the optimization to avoid undesired contact, directly using the reconstruction. By using the reconstruction network, our method can grasp objects from a new camera viewpoint which was not seen during training. Our results show that utilizing learned geometry outperforms alternative formulations for partial-view information based on real robot execution. Our results can be found on this https URL.

Includes members:
  Member "planning multi fingered grasps as probabilistic inference in a learned deep network":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multi fingered grasp planning via inference in deep neural networks")
  Member "generating grasp poses for a high dof gripper using neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep differentiable grasp planner for high dof grippers")
  Member "deepsdf learning continuous signed distance functions for shape representation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a survey on deep geometry learning from a representation perspective")
  Member "robust grasp planning over uncertain shape completions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "kpam sc generalizable manipulation planning using keypoint affordance and shape completion")
  Member "modeling grasp type improves learning based grasp planning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multi fingered grasp planning via inference in deep neural networks")
  Member "robust learning of tactile force estimation through robot interaction":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "in hand object pose tracking via contact feedback and gpu accelerated robotic simulation")
  Member "learning implicit fields for generative shape modeling":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a survey on deep geometry learning from a representation perspective")
  Member "pointconv deep convolutional networks on 3d point clouds":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "review deep learning on 3d 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)