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 "calibration of asynchronous camera networks for object reconstruction tasks"):
  Abstract: Camera network and multi-camera calibration for external parameters is a necessary step for a variety of contexts in computer vision and robotics, ranging from three-dimensional reconstruction to human activity tracking. This paper describes a method for camera network and/or multi-camera calibration suitable for specific contexts: the cameras may not all have a common field of view, or if they do, there may be some views that are 180 degrees from one another, and the network may be asynchronous. The calibration object required is one or more planar calibration patterns, rigidly attached to one another, and are distinguishable from one another, such as aruco or charuco patterns. We formulate the camera network and/or multi-camera calibration problem in this context using rigidity constraints, represented as a system of equations, and an approximate solution is found through a two-step process. Synthetic and real experiments, including scenarios of a asynchronous camera network and rotating imaging system, demonstrate the method in a variety of settings. Reconstruction accuracy error was less than 0.5 mm for all datasets. This method is suitable for new users to calibrate a camera network, and the modularity of the calibration object also allows for disassembly, shipping, and the use of this method in a variety of large and small spaces.

Includes members:
  Member "tree morphology for phenotyping from semantics based mapping in orchard environments":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "semantic mapping for orchard environments by merging two sides reconstructions of tree rows")
  Member "geometric loss functions for camera pose regression with deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "neural mesh refiner for 6 dof pose estimation")
  Member "dpc net deep pose correction for visual localization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deepmel compiling visual multi experience localization into a deep neural network")
  Member "solving the robot world hand eye s calibration problem with iterative methods":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "calibration of asynchronous camera networks calico")
  Member "a robotic vision system to measure tree traits":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "semantic mapping for orchard environments by merging two sides reconstructions of tree rows")
  Member "relative camera pose estimation using convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deepmel compiling visual multi experience localization into a deep neural network")
  Member "panoptic studio a massively multiview system for social interaction capture":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "nonparametric structure regularization machine for 2d hand pose estimation")

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)