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: "sports camera calibration via synthetic data"
  Abstract: Calibrating sports cameras is important for autonomous broadcasting and sports analysis. Here we propose a highly automatic method for calibrating sports cameras from a single image using synthetic data. First, we develop a novel camera pose engine. The camera pose engine has only three significant free parameters so that it can effectively generate a lot of camera poses and corresponding edge (i.e, field marking) images. Then, we learn compact deep features via a siamese network from paired edge image and camera pose and build a feature-pose database. After that, we use a novel two-GAN (generative adversarial network) model to detect field markings in real images. Finally, we query an initial camera pose from the feature-pose database and refine camera poses using truncated distance images. We evaluate our method on both synthetic and real data. Our method not only demonstrates the robustness on the synthetic data but also achieves the state-of-the-art accuracy on a standard soccer dataset and very high performance on a volleyball dataset.

Connected through hyperedge HE-1 (induced by source paper "centrality graph convolutional networks for skeleton based action recognition"):
  with members:
    - "learning class regularized features for action recognition"
    - "semantics guided neural networks for efficient skeleton based human action recognition"
    - "actional structural graph convolutional networks for skeleton based action recognition"
    - "ntu rgb d 120 a large scale benchmark for 3d human activity understanding"
    - "independently recurrent neural network indrnn building a longer and deeper rnn"
    - "real world anomaly detection in surveillance videos"
    - "pose flow efficient online pose tracking"
    - "memory attention networks for skeleton based action recognition"

Connected through hyperedge HE-2 (induced by source paper "optimizing through learned errors for accurate sports field registration"):
  with members:
    - "regnet learning the optimization of direct image to image pose registration"
    - "lf net learning local features from images"
    - "ls net learning to solve nonlinear least squares for monocular stereo"
    - "spectral normalization for generative adversarial networks"
    - "acquisition of localization confidence for accurate object detection"
    - "ba net dense bundle adjustment network"
    - "3d pose regression using convolutional neural networks"
    - "superpoint self supervised interest point detection and description"

Connected through hyperedge HE-3 (induced by source paper "estimating 3d camera pose from 2d pedestrian trajectories"):
  with members:
    - "introduction to camera pose estimation with deep learning"
    - "deep global relative networks for end to end 6 dof visual localization and odometry"
    - "vlocnet deep multitask learning for semantic visual localization and odometry"
    - "inloc indoor visual localization with dense matching and view synthesis"
    - "vidloc a deep spatio temporal model for 6 dof video clip relocalization"
    - "geometric loss functions for camera pose regression with deep learning"
    - "learning less is more 6d camera localization via 3d surface regression"
    - "camera relocalization by predicting pairwise relative poses using convolutional neural network"

Connected through hyperedge HE-4 (induced by source paper "pan tilt zoom slam for sports videos"):
  with members:
    - "panoptic segmentation"
    - "scenecode monocular dense semantic reconstruction using learned encoded scene representations"
    - "let s take this online adapting scene coordinate regression network predictions for online rgb d camera relocalisation"
    - "codeslam learning a compact optimisable representation for dense visual slam"
    - "a two point method for ptz camera calibration in sports"
    - "soccer on your tabletop"
    - "light cascaded convolutional neural networks for accurate player detection"
    - "backtracking regression forests for accurate camera relocalization"

Connected through hyperedge HE-5 (induced by source paper "motion capture from pan tilt cameras with unknown orientation"):
  with members:
    - "a two point method for ptz camera calibration in sports"
    - "what face and body shapes can tell about height"
    - "lifting from the deep convolutional 3d pose estimation from a single image"
    - "harvesting multiple views for marker less 3d human pose annotations"
    - "compositional human pose regression"
    - "a simple yet effective baseline for 3d human pose estimation"
    - "deep multitask architecture for integrated 2d and 3d human sensing"
    - "coarse to fine volumetric prediction for single image 3d human pose"

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)