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: "automated top view registration of broadcast football videos"
  Abstract: In this paper, we propose a novel method to register football broadcast video frames on the static top view model of the playing surface. The proposed method is fully automatic in contrast to the current state of the art which requires manual initialization of point correspondences between the image and the static model. Automatic registration using existing approaches has been difficult due to the lack of sufficient point correspondences. We investigate an alternate approach exploiting the edge information from the line markings on the field. We formulate the registration problem as a nearest neighbour search over a synthetically generated dictionary of edge map and homography pairs. The synthetic dictionary generation allows us to exhaustively cover a wide variety of camera angles and positions and reduce this problem to a minimal per-frame edge map matching procedure. We show that the per-frame results can be improved in videos using an optimization framework for temporal camera stabilization. We demonstrate the efficacy of our approach by presenting extensive results on a dataset collected from matches of football World Cup 2014.

Connected through hyperedge HE-1 (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"
    - "sports camera calibration via synthetic data"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "neuralsampler euclidean point cloud auto encoder and sampler"):
  with members:
    - "adversarial autoencoders for compact representations of 3d point clouds"
    - "dynamic graph cnn for learning on point clouds"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "foldingnet point cloud auto encoder via deep grid deformation"
    - "learning representations and generative models for 3d point clouds"
    - "learning a predictable and generative vector representation for objects"
    - "a point set generation network for 3d object reconstruction from a single image"
    - "learning a probabilistic latent space of object shapes via 3d generative adversarial modeling"

Connected through hyperedge HE-3 (induced by source paper "sports camera calibration via synthetic data"):
  with members:
    - "deep auxiliary learning for visual localization and odometry"
    - "a two point method for ptz camera calibration in sports"
    - "soccer on your tabletop"
    - "backtracking regression forests for accurate camera relocalization"
    - "play and learn using video games to train computer vision models"
    - "image to image translation with conditional adversarial networks"
    - "siamese regression networks with efficient mid level feature extraction for 3d object pose estimation"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-4 (induced by source paper "a two point method for ptz camera calibration in sports"):
  with members:
    - "light cascaded convolutional neural networks for accurate player detection"
    - "on the fly adaptation of regression forests for online camera relocalisation"
    - "backtracking regression forests for accurate camera relocalization"
    - "latch learned arrangements of three patch codes"
    - "continuous localization and mapping of a pan tilt zoom camera for wide area tracking"

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)