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 "in hand object pose tracking via contact feedback and gpu accelerated robotic simulation"):
  Abstract: Tracking the pose of an object while it is being held and manipulated by a robot hand is difficult for vision-based methods due to significant occlusions. Prior works have explored using contact feedback and particle filters to localize in-hand objects. However, they have mostly focused on the static grasp setting and not when the object is in motion, as doing so requires modeling of complex contact dynamics. In this work, we propose using GPU-accelerated parallel robot simulations and derivative-free, sample-based optimizers to track in-hand object poses with contact feedback during manipulation. We use physics simulation as the forward model for robot-object interactions, and the algorithm jointly optimizes for the state and the parameters of the simulations, so they better match with those of the real world. Our method runs in real-time (30Hz) on a single GPU, and it achieves an average point cloud distance error of 6mm in simulation experiments and 13mm in the real-world ones.

Includes members:
  Member "deepim deep iterative matching for 6d pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "neural mesh refiner for 6 dof pose estimation")
  Member "learning to estimate pose and shape of hand held objects from rgb images":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "robust occlusion aware pose estimation for objects grasped by adaptive hands")
  Member "poserbpf a rao blackwellized particle filter for 6d object pose tracking":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deepim deep iterative matching for 6d pose estimation")
  Member "learning joint reconstruction of hands and manipulated objects":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "robust occlusion aware pose estimation for objects grasped by adaptive hands")
  Member "joint inference of kinematic and force trajectories with visuo tactile sensing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "making sense of vision and touch learning multimodal representations for contact rich tasks")
  Member "making sense of vision and touch learning multimodal representations for contact rich tasks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning precise 3d manipulation from multiple uncalibrated cameras")
  Member "non smooth newton methods for deformable multi body dynamics":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "inferring the material properties of granular media for robotic tasks")
  Member "learning dexterous in hand manipulation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "never give up learning directed exploration strategies")

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)