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: "a two point method for ptz camera calibration in sports"
  Abstract: Calibrating narrow field of view soccer cameras is challenging because there are very few field markings in the image. Unlike previous solutions, we propose a two-point method, which requires only two point correspondences given the prior knowledge of base location and orientation of a pan-tilt-zoom (PTZ) camera. We deploy this new calibration method to annotate pan-tilt-zoom data from soccer videos. The collected data are used as references for new images. We also propose a fast random forest method to predict pan-tilt angles without image-to-image feature matching, leading to an efficient calibration method for new images. We demonstrate our system on synthetic data and two real soccer datasets. Our two-point approach achieves superior performance over the state-of-the-art method.

Connected through hyperedge HE-1 (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"
    - "sports camera calibration via synthetic data"
    - "codeslam learning a compact optimisable representation for dense visual slam"
    - "soccer on your tabletop"
    - "light cascaded convolutional neural networks for accurate player detection"
    - "backtracking regression forests for accurate camera relocalization"

Connected through hyperedge HE-2 (induced by source paper "motion capture from pan tilt cameras with unknown orientation"):
  with members:
    - "sports camera calibration via synthetic data"
    - "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"

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"
    - "soccer on your tabletop"
    - "backtracking regression forests for accurate camera relocalization"
    - "automated top view registration of broadcast football videos"
    - "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 "exploiting points and lines in regression forests for rgb d camera relocalization"):
  with members:
    - "geometric loss functions for camera pose regression with deep learning"
    - "learning less is more 6d camera localization via 3d surface regression"
    - "on the fly adaptation of regression forests for online camera relocalisation"
    - "autonomous mobile robot navigation in uneven and unstructured indoor environments"
    - "backtracking regression forests for accurate camera relocalization"
    - "image based localization using lstms for structured feature correlation"
    - "bundlefusion real time globally consistent 3d reconstruction using on the fly surface re integration"
    - "modelling uncertainty in deep learning for camera relocalization"

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)