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 "image matching across wide baselines from paper to practice"):
  Abstract: We introduce a comprehensive benchmark for local features and robust estimation algorithms, focusing on the downstream task -- the accuracy of the reconstructed camera pose -- as our primary metric. Our pipeline's modular structure allows us to easily integrate, configure, and combine methods and heuristics. We demonstrate this by embedding dozens of popular algorithms and evaluating them, from seminal works to the cutting edge of machine learning research. We show that with proper settings, classical solutions may still outperform the perceived state of the art. #R##N#Besides establishing the actual state of the art, the experiments conducted in this paper reveal unexpected properties of SfM pipelines that can be exploited to help improve their performance, for both algorithmic and learned methods. Data and code are online this https URL, providing an easy-to-use and flexible framework for the benchmarking of local feature and robust estimation methods, both alongside and against top-performing methods. This work provides the basis for an open challenge on wide-baseline image matching this https URL .

Includes members:
  Member "self supervised learning of depth and ego motion with differentiable bundle adjustment":
    also connected through other hyperedges:
      (none)
  Member "d2 net a trainable cnn for joint detection and description of local features":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "visual camera re localization from rgb and rgb d images using dsac")
  Member "explicit spatial encoding for deep local descriptors":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "unifying deep local and global features for image search")
  Member "an evaluation of feature matchers forfundamental matrix estimation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "extremely dense point correspondences using a learned feature descriptor")
  Member "contextdesc local descriptor augmentation with cross modality context":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "unifying deep local and global features for image search")
  Member "neural guided ransac learning where to sample model hypotheses":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "visual camera re localization from rgb and rgb d images using dsac")
  Member "nm net mining reliable neighbors for robust feature correspondences":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "attentive context normalization for robust permutation equivariant learning")
  Member "understanding the limitations of cnn based absolute camera pose regression":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "visual camera re localization from rgb and rgb d images using dsac")

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)