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 "degeneracy in self calibration revisited and a deep learning solution for uncalibrated slam"):
  Abstract: Self-calibration of camera intrinsics and radial distortion has a long history of research in the computer vision community. However, it remains rare to see real applications of such techniques to modern Simultaneous Localization And Mapping (SLAM) systems, especially in driving scenarios. In this paper, we revisit the geometric approach to this problem, and provide a theoretical proof that explicitly shows the ambiguity between radial distortion and scene depth when two-view geometry is used to self-calibrate the radial distortion. In view of such geometric degeneracy, we propose a learning approach that trains a convolutional neural network (CNN) on a large amount of synthetic data. We demonstrate the utility of our proposed method by applying it as a checkerboard-free calibration tool for SLAM, achieving comparable or superior performance to previous learning and hand-crafted methods.

Includes members:
  Member "grad cam visual explanations from deep networks via gradient based localization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "causal interpretability for machine learning problems methods and evaluation")
  Member "baseline desensitizing in translation averaging":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "graph based parallel large scale structure from motion")
  Member "learning 2d to 3d lifting for object detection in 3d for autonomous vehicles":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "monolayout amodal scene layout from a single image")
  Member "rolling shutter aware differential sfm and image rectification":
    also connected through other hyperedges:
      (none)
  Member "hierarchical metric learning and matching for 2d and 3d geometric correspondences":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "r2d2 repeatable and reliable detector and descriptor")
  Member "deep supervision with intermediate concepts":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deeply supervised knowledge synergy")
  Member "fisheyerecnet a multi context collaborative deep network for fisheye image rectification":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "mini unmanned aerial vehicle based remote sensing techniques applications and prospects")
  Member "challenges in monocular visual odometry photometric calibration motion bias and rolling shutter effect":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "d3vo deep depth deep pose and deep uncertainty for monocular visual odometry")

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)