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 "omnislam omnidirectional localization and dense mapping for wide baseline multi camera systems"):
  Abstract: In this paper, we present an omnidirectional localization and dense mapping system for a wide-baseline multiview stereo setup with ultra-wide field-of-view (FOV) fisheye cameras, which has a 360 degrees coverage of stereo observations of the environment. For more practical and accurate reconstruction, we first introduce improved and light-weighted deep neural networks for the omnidirectional depth estimation, which are faster and more accurate than the existing networks. Second, we integrate our omnidirectional depth estimates into the visual odometry (VO) and add a loop closing module for global consistency. Using the estimated depth map, we reproject keypoints onto each other view, which leads to a better and more efficient feature matching process. Finally, we fuse the omnidirectional depth maps and the estimated rig poses into the truncated signed distance function (TSDF) volume to acquire a 3D map. We evaluate our method on synthetic datasets with ground-truth and real-world sequences of challenging environments, and the extensive experiments show that the proposed system generates excellent reconstruction results in both synthetic and real-world environments.

Includes members:
  Member "tightly coupled 3d lidar inertial odometry and mapping":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "monocular direct sparse localization in a prior 3d surfel map")
  Member "omnimvs end to end learning for omnidirectional stereo matching":
    also connected through other hyperedges:
      (none)
  Member "ga net guided aggregation net for end to end stereo matching":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "dsgn deep stereo geometry network for 3d object detection")
  Member "rovo robust omnidirectional visual odometry for wide baseline wide fov camera systems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "restricted deformable convolution based road scene semantic segmentation using surround view cameras")
  Member "sweepnet wide baseline omnidirectional depth estimation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "monocular 3d object detection in cylindrical images from fisheye cameras")
  Member "occlusions motion and depth boundaries with a generic network for disparity optical flow or scene flow estimation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "belief propagation reloaded learning bp layers for labeling problems")
  Member "real time dense mapping for self driving vehicles using fisheye cameras":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "project autovision localization and 3d scene perception for an autonomous vehicle with a multi camera system")
  Member "omnidirectional dso direct sparse odometry with fisheye cameras":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "rovo robust omnidirectional visual odometry for wide baseline wide fov camera systems")

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)