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 "silhonet fisheye adaptation of a roi based object pose estimation network to monocular fisheye images"):
  Abstract: There has been much recent interest in deep learning methods for monocular image based object pose estimation. While object pose estimation is an important problem for autonomous robot interaction with the physical world, and the application space for monocular-based methods is expansive, there has been little work on applying these methods with fisheye imaging systems. Also, little exists in the way of annotated fisheye image datasets on which these methods can be developed and tested. The research landscape is even more sparse for object detection methods applied in the underwater domain, fisheye image based or otherwise. In this work, we present a novel framework for adapting a ROI-based 6D object pose estimation method to work on full fisheye images. The method incorporates the gnomic projection of regions of interest from an intermediate spherical image representation to correct for the fisheye distortions. Further, we contribute a fisheye image dataset, called UWHandles, collected in natural underwater environments, with 6D object pose and 2D bounding box annotations.

Includes members:
  Member "woodscape a multi task multi camera fisheye dataset for autonomous driving":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "fisheyedistancenet self supervised scale aware distance estimation using monocular fisheye camera for autonomous driving")
  Member "underwater fish detection with weak multi domain supervision":
    also connected through other hyperedges:
      (none)
  Member "datasets for face and object detection in fisheye images":
    also connected through other hyperedges:
      (none)
  Member "spherical cnns on unstructured grids":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "variable viewpoint representations for 3d object recognition")
  Member "spherical u net on cortical surfaces methods and applications":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "gauge equivariant mesh cnns anisotropic convolutions on geometric graphs")
  Member "tagslam robust slam with fiducial markers":
    also connected through other hyperedges:
      (none)
  Member "convolutions on spherical images":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "pano popups indoor 3d reconstruction with a plane aware network")
  Member "reprojection r cnn a fast and accurate object detector for 360 images":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "monocular 3d object detection in cylindrical images from fisheye cameras")

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)