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: "multi image matching via fast alternating minimization"
  Abstract: In this paper we propose a global optimization-based approach to jointly matching a set of images. The estimated correspondences simultaneously maximize pairwise feature affinities and cycle consistency across multiple images. Unlike previous convex methods relying on semidefinite programming, we formulate the problem as a low-rank matrix recovery problem and show that the desired semidefiniteness of a solution can be spontaneously fulfilled. The low-rank formulation enables us to derive a fast alternating minimization algorithm in order to handle practical problems with thousands of features. Both simulation and real experiments demonstrate that the proposed algorithm can achieve a competitive performance with an order of magnitude speedup compared to the state-of-the-art algorithm. In the end, we demonstrate the applicability of the proposed method to match the images of different object instances and as a result the potential to reconstruct category-specific object models from those images.

Connected through hyperedge HE-1 (induced by source paper "crdoco pixel level domain transfer with cross domain consistency"):
  with members:
    - "recover and identify a generative dual model for cross resolution person re identification"
    - "learning correspondence from the cycle consistency of time"
    - "learning resolution invariant deep representations for person re identification"
    - "show match and segment joint learning of semantic matching and object co segmentation"
    - "model adaptation with synthetic and real data for semantic dense foggy scene understanding"
    - "learning to adapt structured output space for semantic segmentation"
    - "df net unsupervised joint learning of depth and flow using cross task consistency"
    - "deepmvs learning multi view stereopsis"

Connected through hyperedge HE-2 (induced by source paper "learning correspondence from the cycle consistency of time"):
  with members:
    - "recycle gan unsupervised video retargeting"
    - "deep clustering for unsupervised learning of visual features"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "efficient video object segmentation via network modulation"
    - "adaptive temporal encoding network for video instance level human parsing"
    - "switchable temporal propagation network"
    - "tracking emerges by colorizing videos"
    - "representation learning with contrastive predictive coding"

Connected through hyperedge HE-3 (induced by source paper "temporal cycle consistency learning"):
  with members:
    - "scaling egocentric vision the epic kitchens dataset"
    - "action completion a temporal model for moment detection"
    - "playing hard exploration games by watching youtube"
    - "unsupervised learning and segmentation of complex activities from video"
    - "neighbourhood consensus networks"
    - "deep unsupervised learning of visual similarities"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "cycada cycle consistent adversarial domain adaptation"

Connected through hyperedge HE-4 (induced by source paper "show match and segment joint learning of semantic matching and object co segmentation"):
  with members:
    - "semi supervised semantic matching"
    - "feelvos fast end to end embedding learning for video object segmentation"
    - "semantic aware attention based deep object co segmentation"
    - "recurrent transformer networks for semantic correspondence"
    - "df net unsupervised joint learning of depth and flow using cross task consistency"
    - "deepmvs learning multi view stereopsis"
    - "self supervised learning of geometrically stable features through probabilistic introspection"
    - "multimodal unsupervised image to image translation"

Connected through hyperedge HE-5 (induced by source paper "fast and robust multi person 3d pose estimation from multiple views"):
  with members:
    - "ordinal depth supervision for 3d human pose estimation"
    - "a generalizable approach for multi view 3d human pose regression"
    - "learning to estimate 3d human pose and shape from a single color image"
    - "total capture a 3d deformation model for tracking faces hands and bodies"
    - "multiposenet fast multi person pose estimation using pose residual network"
    - "camera style adaptation for person re identification"
    - "towards 3d human pose estimation in the wild a weakly supervised approach"
    - "lifting from the deep convolutional 3d pose estimation from a single image"

Connected through hyperedge HE-6 (induced by source paper "rgb d individual segmentation"):
  with members:
    - "lf net learning local features from images"
    - "srda generating instance segmentation annotation via scanning reasoning and domain adaptation"
    - "applying domain randomization to synthetic data for object category detection"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "neighbourhood consensus networks"
    - "semi parametric image synthesis"
    - "path aggregation network for instance segmentation"
    - "sniper efficient multi scale training"

Connected through hyperedge HE-7 (induced by source paper "coherent point drift networks unsupervised learning of non rigid point set registration"):
  with members:
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "so net self organizing network for point cloud analysis"
    - "an unsupervised learning model for deformable medical image registration"
    - "began boundary equilibrium generative adversarial networks"
    - "feastnet feature steered graph convolutions for 3d shape analysis"
    - "convolutional neural network architecture for geometric matching"
    - "deepsketch2face a deep learning based sketching system for 3d face and caricature modeling"
    - "foldingnet interpretable unsupervised learning on 3d point clouds"

Connected through hyperedge HE-8 (induced by source paper "probabilistic permutation synchronization using the riemannian structure of the birkhoff polytope"):
  with members:
    - "synchronisation of partial multi matchings via non negative factorisations"
    - "ppfnet global context aware local features for robust 3d point matching"
    - "bayesian pose graph optimization via bingham distributions and tempered geodesic mcmc"
    - "averaging stochastic gradient descent on riemannian manifolds"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "cad priors for accurate and flexible instance reconstruction"
    - "consistent multiple graph matching with multi layer random walks synchronization"
    - "multi image semantic matching by mining consistent features"

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)