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 "real time soft robot 3d proprioception via deep vision based sensing"):
  Abstract: The soft robots are welcomed in many robotic applications because of their high flexibility, which also poses a long-standing challenge on their proprioception, or measuring the real-time 3D shapes of the soft robots from internal sensors. The challenge exists in both the sensor design and robot modeling. In this paper, we propose a framework to measure the real-time high-resolution 3D shapes of soft robots. The framework is based on an embedded camera to capture the inside/outside patterns of the robots under different loading conditions, and a CNN to produce a latent code representing the robot state, which can then be used to reconstruct the 3D shape using a neural network improved from FoldingNet. We tested the framework on four different soft actuators with various kinds of deformations, and achieved real-time computation ($<$2ms/frame) for robust shape estimation of high precision ($<$5% relative error for 2025 points) at an arbitrary resolution. We believe the method could be widely applied to different designs of soft robots for proprioception, and enabling people to better control them under complicated environments. Our code is available at this https URL.

Includes members:
  Member "deformation capture via soft and stretchable sensor arrays":
    also connected through other hyperedges:
      (none)
  Member "deformation capture via self sensing capacitive arrays":
    also connected through other hyperedges:
      (none)
  Member "pix3d dataset and methods for single image 3d shape modeling":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "total3dunderstanding joint layout object pose and mesh reconstruction for indoor scenes from a single image")
  Member "densepose dense human pose estimation in the wild":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "neural human video rendering joint learning of dynamic textures and rendering to video translation")
  Member "foldingnet interpretable unsupervised learning on 3d point clouds":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "advectivenet an eulerian lagrangian fluidic reservoir for point cloud processing")
  Member "3d r2n2 a unified approach for single and multi view 3d object reconstruction":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "pt2pc learning to generate 3d point cloud shapes from part tree conditions")
  Member "a point set generation network for 3d object reconstruction from a single image":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "pt2pc learning to generate 3d point cloud shapes from part tree conditions")
  Member "deep residual learning for image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "discrimination aware network pruning for deep model compression")

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)