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 "consac robust multi model fitting by conditional sample consensus"):
  Abstract: We present a robust estimator for fitting multiple parametric models of the same form to noisy measurements. Applications include finding multiple vanishing points in man-made scenes, fitting planes to architectural imagery, or estimating multiple rigid motions within the same sequence. In contrast to previous works, which resorted to hand-crafted search strategies for multiple model detection, we learn the search strategy from data. A neural network conditioned on previously detected models guides a RANSAC estimator to different subsets of all measurements, thereby finding model instances one after another. We train our method supervised as well as self-supervised. For supervised training of the search strategy, we contribute a new dataset for vanishing point estimation. Leveraging this dataset, the proposed algorithm is superior with respect to other robust estimators as well as to designated vanishing point estimation algorithms. For self-supervised learning of the search, we evaluate the proposed algorithm on multi-homography estimation and demonstrate an accuracy that is superior to state-of-the-art methods.

Includes members:
  Member "neural guided ransac learning where to sample model hypotheses":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "image matching across wide baselines from paper to practice")
  Member "progressive x efficient anytime multi model fitting algorithm":
    also connected through other hyperedges:
      (none)
  Member "learning less is more 6d camera localization via 3d surface regression":
    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 "geometric multi model fitting with a convex relaxation algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a new method for atlanta world frame estimation")
  Member "augmented reality meets computer vision efficient data generation for urban driving scenes":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "cars can t fly up in the sky improving urban scene segmentation via height driven attention networks")
  Member "orb slam2 an open source slam system for monocular stereo and rgb d cameras":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "perception and decision making of autonomous systems in the era of learning an overview")
  Member "multi class model fitting by energy minimization and mode seeking":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning for multi type subspace clustering")
  Member "deep learning for vanishing point detection using an inverse gnomonic projection":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "neural guided ransac learning where to sample model hypotheses")

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)