human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "light cascaded convolutional neural networks for accurate player detection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "pan tilt zoom slam for sports videos")
  Member "on the fly adaptation of regression forests for online camera relocalisation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "visual camera re localization from rgb and rgb d images using dsac")
  Member "backtracking regression forests for accurate camera relocalization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "visual camera re localization from rgb and rgb d images using dsac")
  Member "automated top view registration of broadcast football videos":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "optimizing through learned errors for accurate sports field registration")
  Member "latch learned arrangements of three patch codes":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "features for ground texture based localization a survey")
  Member "continuous localization and mapping of a pan tilt zoom camera for wide area tracking":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "pan tilt zoom slam for sports videos")

Question: Please tell me which class the source paper 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)