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: "3d robot pose estimation from 2d images"
  Abstract: This paper considers the task of locating articulated poses of multiple robots in images. Our approach simultaneously infers the number of robots in a scene, identifies joint locations and estimates sparse depth maps around joint locations. The proposed method applies staged convolutional feature detectors to 2D image inputs and computes robot instance masks using a recurrent network architecture. In addition, regression maps of most likely joint locations in pixel coordinates together with depth information are computed. Compositing 3D robot joint kinematics is accomplished by applying masks to joint readout maps. Our end-to-end formulation is in contrast to previous work in which the composition of robot joints into kinematics is performed in a separate post-processing step. Despite the fact that our models are trained on artificial data, we demonstrate generalizability to real world images.

Connected through hyperedge HE-1 (induced by source paper "referencing between a head mounted device and robotic manipulators"):
  with members:
    - "computationally efficient dense moving object detection based on reduced space disparity estimation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "yolo9000 better faster stronger"
    - "refinenet multi path refinement networks for high resolution semantic segmentation"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "end to end defect detection in automated fiber placement based on artificially generated data"):
  with members:
    - "ganerated hands for real time 3d hand tracking from monocular rgb"
    - "driving in the matrix can virtual worlds replace human generated annotations for real world tasks"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"
    - "u net convolutional networks for biomedical image segmentation"
    - "semantic image segmentation with deep convolutional nets and fully connected crfs"
    - "learning deep object detectors from 3d models"

Connected through hyperedge HE-3 (induced by source paper "learning to predict robot keypoints using artificially generated images"):
  with members:
    - "two stage transfer learning for heterogeneous robot detection and 3d joint position estimation in a 2d camera image using cnn"
    - "rendergan generating realistic labeled data"
    - "image to image translation with conditional adversarial networks"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-4 (induced by source paper "enhanced human machine interaction by combining proximity sensing with global perception"):
  with members:
    - "large area 3d human pose detection via stereo reconstruction in panoramic cameras"
    - "realtime multi person 2d pose estimation using part affinity fields"

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.CV(Computer Vision and Pattern Recognition)