human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "an evaluation of feature matchers forfundamental matrix estimation"
  Abstract: Matching two images while estimating their relative geometry is a key step in many computer vision applications. For decades, a well-established pipeline, consisting of SIFT, RANSAC, and 8-point algorithm, has been used for this task. Recently, many new approaches were proposed and shown to outperform previous alternatives on standard benchmarks, including the learned features, correspondence pruning algorithms, and robust estimators. However, whether it is beneficial to incorporate them into the classic pipeline is less-investigated. To this end, we are interested in i) evaluating the performance of these recent algorithms in the context of image matching and epipolar geometry estimation, and ii) leveraging them to design more practical registration systems. The experiments are conducted in four large-scale datasets using strictly defined evaluation metrics, and the promising results provide insight into which algorithms suit which scenarios. According to this, we propose three high-quality matching systems and a Coarse-to-Fine RANSAC estimator. They show remarkable performances and have potentials to a large part of computer vision tasks. To facilitate future research, the full evaluation pipeline and the proposed methods are made publicly available.

Connected through hyperedge HE-1 (induced by source paper "image matching across wide baselines from paper to practice"):
  with members:
    - "self supervised learning of depth and ego motion with differentiable bundle adjustment"
    - "d2 net a trainable cnn for joint detection and description of local features"
    - "explicit spatial encoding for deep local descriptors"
    - "contextdesc local descriptor augmentation with cross modality context"
    - "neural guided ransac learning where to sample model hypotheses"
    - "nm net mining reliable neighbors for robust feature correspondences"
    - "understanding the limitations of cnn based absolute camera pose regression"
    - "adversarial networks for camera pose regression and refinement"

Connected through hyperedge HE-2 (induced by source paper "extremely dense point correspondences using a learned feature descriptor"):
  with members:
    - "d2 net a trainable cnn for joint detection and description of local features"
    - "defslam tracking and mapping of deforming scenes from monocular sequences"
    - "3d reconstruction of whole stomach from endoscope video using structure from motion"
    - "mis slam real time large scale dense deformable slam system in minimal invasive surgery based on heterogeneous computing"
    - "geodesc learning local descriptors by integrating geometry constraints"
    - "normface l 2 hypersphere embedding for face verification"
    - "focal loss for dense object detection"
    - "working hard to know your neighbor s margins local descriptor learning loss"

Connected through hyperedge HE-3 (induced by source paper "visual odometry revisited what should be learnt"):
  with members:
    - "self supervised learning for single view depth and surface normal estimation"
    - "unsupervised scale consistent depth and ego motion learning from monocular video"
    - "pose graph optimization for unsupervised monocular visual odometry"
    - "deeptam deep tracking and mapping"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "deep ordinal regression network for monocular depth estimation"
    - "cnn svo improving the mapping in semi direct visual odometry using single image depth prediction"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"

Connected through hyperedge HE-4 (induced by source paper "enforcing geometric constraints of virtual normal for depth prediction"):
  with members:
    - "knowledge adaptation for efficient semantic segmentation"
    - "decoders matter for semantic segmentation data dependent decoding enables flexible feature aggregation"
    - "fcos fully convolutional one stage object detection"
    - "deep attention based classification network for robust depth prediction"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "rethinking monocular depth estimation with adversarial training"
    - "learning a discriminative feature network for semantic segmentation"
    - "deep ordinal regression network for monocular depth estimation"

Connected through hyperedge HE-5 (induced by source paper "self training with progressive augmentation for unsupervised cross domain person re identification"):
  with members:
    - "regularizing proxies with multi adversarial training for unsupervised domain adaptive semantic segmentation"
    - "eanet enhancing alignment for cross domain person re identification"
    - "multi level factorisation net for person re identification"
    - "harmonious attention network for person re identification"
    - "attention aware compositional network for person re identification"
    - "unsupervised cross dataset person re identification by transfer learning of spatial temporal patterns"
    - "transferable joint attribute identity deep learning for unsupervised person re identification"
    - "person search via a mask guided two stream cnn model"

Connected through hyperedge HE-6 (induced by source paper "unsupervised scale consistent depth and ego motion learning from monocular video"):
  with members:
    - "enforcing geometric constraints of virtual normal for depth prediction"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "joint unsupervised learning of optical flow and depth by watching stereo videos"
    - "df net unsupervised joint learning of depth and flow using cross task consistency"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "hierarchical discrete distribution decomposition for match density estimation"
    - "self supervised learning for dense depth estimation in monocular endoscopy"

Connected through hyperedge HE-7 (induced by source paper "magsac a fast reliable and accurate robust estimator"):
  with members:
    - "magsac marginalizing sample consensus"
    - "graph cut ransac"
    - "mods fast and robust method for two view matching"

Question: Please tell me which class the center node 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)