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: "fast and robust multi person 3d pose estimation from multiple views"
  Abstract: This paper addresses the problem of 3D pose estimation for multiple people in a few calibrated camera views. The main challenge of this problem is to find the cross-view correspondences among noisy and incomplete 2D pose predictions. Most previous methods address this challenge by directly reasoning in 3D using a pictorial structure model, which is inefficient due to the huge state space. We propose a fast and robust approach to solve this problem. Our key idea is to use a multi-way matching algorithm to cluster the detected 2D poses in all views. Each resulting cluster encodes 2D poses of the same person across different views and consistent correspondences across the keypoints, from which the 3D pose of each person can be effectively inferred. The proposed convex optimization based multi-way matching algorithm is efficient and robust against missing and false detections, without knowing the number of people in the scene. Moreover, we propose to combine geometric and appearance cues for cross-view matching. The proposed approach achieves significant performance gains from the state-of-the-art (96.3% vs. 90.6% and 96.9% vs. 88% on the Campus and Shelf datasets, respectively), while being efficient for real-time applications.

Connected through hyperedge HE-1 (induced by source paper "4d association graph for realtime multi person motion capture using multiple video cameras"):
  with members:
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "personlab person pose estimation and instance segmentation with a bottom up part based geometric embedding model"
    - "multi scale structure aware network for human pose estimation"
    - "simple baselines for human pose estimation and tracking"
    - "crowdpose efficient crowded scenes pose estimation and a new benchmark"
    - "efficient online multi person 2d pose tracking with recurrent spatio temporal affinity fields"
    - "multiposenet fast multi person pose estimation using pose residual network"
    - "end to end learning for graph decomposition"

Connected through hyperedge HE-2 (induced by source paper "synergetic reconstruction from 2d pose and 3d motion for wide space multi person video motion capture in the wild"):
  with members:
    - "video motion capture from the part confidence maps of multi camera images by spatiotemporal filtering using the human skeletal model"
    - "pifpaf composite fields for human pose estimation"
    - "deep high resolution representation learning for human pose estimation"
    - "total capture a 3d deformation model for tracking faces hands and bodies"
    - "simple baselines for human pose estimation and tracking"
    - "monocular total capture posing face body and hands in the wild"
    - "towards 3d human pose estimation in the wild a weakly supervised approach"
    - "end to end recovery of human shape and pose"

Connected through hyperedge HE-3 (induced by source paper "cross view tracking for multi human 3d pose estimation at over 100 fps"):
  with members:
    - "cross view fusion for 3d human pose estimation"
    - "learnable triangulation of human pose"
    - "rethinking pose in 3d multi stage refinement and recovery for markerless motion capture"
    - "3d human pose estimation in video with temporal convolutions and semi supervised training"
    - "simple baselines for human pose estimation and tracking"
    - "exploiting temporal information for 3d human pose estimation"
    - "single shot multi person 3d pose estimation from monocular rgb"
    - "harvesting multiple views for marker less 3d human pose annotations"

Connected through hyperedge HE-4 (induced by source paper "deepfuse an imu aware network for real time 3d human pose estimation from multi view image"):
  with members:
    - "self supervised learning of 3d human pose using multi view geometry"
    - "rethinking pose in 3d multi stage refinement and recovery for markerless motion capture"
    - "a generalizable approach for multi view 3d human pose regression"
    - "multi scale structure aware network for human pose estimation"
    - "deep autoencoder for combined human pose estimation and body model upscaling"
    - "2d 3d pose estimation and action recognition using multitask deep learning"
    - "3d human pose estimation with 2d marginal heatmaps"
    - "numerical coordinate regression with convolutional neural networks"

Connected through hyperedge HE-5 (induced by source paper "single stage multi person pose machines"):
  with members:
    - "lcr net multi person 2d and 3d pose detection in natural images"
    - "personlab person pose estimation and instance segmentation with a bottom up part based geometric embedding model"
    - "learning to refine human pose estimation"
    - "single shot multi person 3d pose estimation from monocular rgb"
    - "towards accurate multi person pose estimation in the wild"
    - "pose normalized image generation for person re identification"
    - "joint multi person pose estimation and semantic part segmentation"
    - "mask r cnn"

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)