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 "form2fit learning shape priors for generalizable assembly from disassembly"):
  Abstract: Is it possible to learn policies for robotic assembly that can generalize to new objects? We explore this idea in the context of the kit assembly task. Since classic methods rely heavily on object pose estimation, they often struggle to generalize to new objects without 3D CAD models or task-specific training data. In this work, we propose to formulate the kit assembly task as a shape matching problem, where the goal is to learn a shape descriptor that establishes geometric correspondences between object surfaces and their target placement locations from visual input. This formulation enables the model to acquire a broader understanding of how shapes and surfaces fit together for assembly -- allowing it to generalize to new objects and kits. To obtain training data for our model, we present a self-supervised data-collection pipeline that obtains ground truth object-to-placement correspondences by disassembling complete kits. Our resulting real-world system, Form2Fit, learns effective pick and place strategies for assembling objects into a variety of kits -- achieving $90\%$ average success rates under different initial conditions (e.g. varying object and kit poses), $94\%$ success under new configurations of multiple kits, and over $86\%$ success with completely new objects and kits.

Includes members:
  Member "normalized object coordinate space for category level 6d object pose and size estimation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "cps class level 6d pose and shape estimation from monocular images")
  Member "learning pose estimation for high precision robotic assembly using simulated depth images":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning precise 3d manipulation from multiple uncalibrated cameras")
  Member "learning robotic assembly from cad":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning precise 3d manipulation from multiple uncalibrated cameras")
  Member "tom net learning transparent object matting from a single image":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "indices matter learning to index for deep image matting")
  Member "time reversal as self supervision":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "robonet large scale multi robot learning")
  Member "data efficient deep reinforcement learning for dexterous manipulation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning precise 3d manipulation from multiple uncalibrated cameras")
  Member "learning synergies between pushing and grasping with self supervised deep reinforcement learning":
    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 "dense object nets learning dense visual object descriptors by and for robotic manipulation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "dgcm net dense geometrical correspondence matching network for incremental experience based robotic grasping")

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)