human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "learning pose estimation for high precision robotic assembly using simulated depth images"
  Abstract: Most of industrial robotic assembly tasks today require fixed initial conditions for successful assembly. These constraints induce high production costs and low adaptability to new tasks. In this work we aim towards flexible and adaptable robotic assembly by using 3D CAD models for all parts to be assembled. We focus on a generic assembly task - the Siemens Innovation Challenge - in which a robot needs to assemble a gear-like mechanism with high precision into an operating system. To obtain the millimeter-accuracy required for this task and industrial settings alike, we use a depth camera mounted near the robot end-effector. We present a high-accuracy two-stage pose estimation procedure based on deep convolutional neural networks, which includes detection, pose estimation, refinement, and handling of near- and full symmetries of parts. The networks are trained on simulated depth images with means to ensure successful transfer to the real robot. We obtain an average pose estimation error of 2.16 millimeters and 0.64 degree leading to 91% success rate for robotic assembly of randomly distributed parts. To the best of our knowledge, this is the first time that the Siemens Innovation Challenge is fully addressed, with all the parts assembled with high success rates.

Connected through hyperedge HE-1 (induced by source paper "learning precise 3d manipulation from multiple uncalibrated cameras"):
  with members:
    - "pixel attentive policy gradient for multi fingered grasping in cluttered scenes"
    - "kpam keypoint affordances for category level robotic manipulation"
    - "a review of robot learning for manipulation challenges representations and algorithms"
    - "making sense of vision and touch learning multimodal representations for contact rich tasks"
    - "reinforcement learning on variable impedance controller for high precision robotic assembly"
    - "grip generative robust inference and perception for semantic robot manipulation in adversarial environments"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "learning robotic assembly from cad"

Connected through hyperedge HE-2 (induced by source paper "form2fit learning shape priors for generalizable assembly from disassembly"):
  with members:
    - "normalized object coordinate space for category level 6d object pose and size estimation"
    - "learning robotic assembly from cad"
    - "tom net learning transparent object matting from a single image"
    - "time reversal as self supervision"
    - "data efficient deep reinforcement learning for dexterous manipulation"
    - "learning synergies between pushing and grasping with self supervised deep reinforcement learning"
    - "dense object nets learning dense visual object descriptors by and for robotic manipulation"
    - "dilated residual networks"

Question: Please tell me which class the center node 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)