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: "dense semantic correspondence where every pixel is a classifier"
  Abstract: Determining dense semantic correspondences across objects and scenes is a difficult problem that underpins many higher-level computer vision algorithms. Unlike canonical dense correspondence problems which consider images that are spatially or temporally adjacent, semantic correspondence is characterized by images that share similar high-level structures whose exact appearance and geometry may differ. #R##N#Motivated by object recognition literature and recent work on rapidly estimating linear classifiers, we treat semantic correspondence as a constrained detection problem, where an exemplar LDA classifier is learned for each pixel. LDA classifiers have two distinct benefits: (i) they exhibit higher average precision than similarity metrics typically used in correspondence problems, and (ii) unlike exemplar SVM, can output globally interpretable posterior probabilities without calibration, whilst also being significantly faster to train. #R##N#We pose the correspondence problem as a graphical model, where the unary potentials are computed via convolution with the set of exemplar classifiers, and the joint potentials enforce smoothly varying correspondence assignment.

Connected through hyperedge HE-1 (induced by source paper "learning semantic correspondence exploiting an object level prior"):
  with members:
    - "sfnet learning object aware semantic correspondence"
    - "hyperpixel flow semantic correspondence with multi layer neural features"
    - "learning correspondence from the cycle consistency of time"
    - "recurrent transformer networks for semantic correspondence"
    - "df net unsupervised joint learning of depth and flow using cross task consistency"
    - "self supervised learning of geometrically stable features through probabilistic introspection"
    - "neighbourhood consensus networks"
    - "attentive semantic alignment with offset aware correlation kernels"

Connected through hyperedge HE-2 (induced by source paper "scops self supervised co part segmentation"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "discovery of latent 3d keypoints via end to end geometric reasoning"
    - "unsupervised discovery of object landmarks as structural representations"
    - "deep feature factorization for concept discovery"
    - "unsupervised learning of object frames by dense equivariant image labelling"
    - "network dissection quantifying interpretability of deep visual representations"
    - "convolutional neural network architecture for geometric matching"
    - "improving landmark localization with semi supervised learning"

Connected through hyperedge HE-3 (induced by source paper "hyperpixel flow semantic correspondence with multi layer neural features"):
  with members:
    - "semantic matching by weakly supervised 2d point set registration"
    - "exploring randomly wired neural networks for image recognition"
    - "parn pyramidal affine regression networks for dense semantic correspondence"
    - "recurrent transformer networks for semantic correspondence"
    - "self supervised learning of geometrically stable features through probabilistic introspection"
    - "neighbourhood consensus networks"
    - "hierarchical metric learning and matching for 2d and 3d geometric correspondences"
    - "attentive semantic alignment with offset aware correlation kernels"

Connected through hyperedge HE-4 (induced by source paper "semantic attribute matching networks"):
  with members:
    - "neural best buddies sparse cross domain correspondence"
    - "recurrent transformer networks for semantic correspondence"
    - "a closed form solution to photorealistic image stylization"
    - "neighbourhood consensus networks"
    - "arbitrary style transfer with deep feature reshuffle"
    - "convolutional neural network architecture for geometric matching"
    - "anchornet a weakly supervised network to learn geometry sensitive features for semantic matching"
    - "diversified texture synthesis with feed forward networks"

Connected through hyperedge HE-5 (induced by source paper "one framework to register them all pointnet encoding for point cloud alignment"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "deepicp an end to end deep neural network for 3d point cloud registration"
    - "3dregnet a deep neural network for 3d point registration"
    - "pointnetlk robust efficient point cloud registration using pointnet"
    - "deep closest point learning representations for point cloud registration"
    - "densefusion 6d object pose estimation by iterative dense fusion"
    - "the perfect match 3d point cloud matching with smoothed densities"
    - "3dfeat net weakly supervised local 3d features for point cloud registration"

Connected through hyperedge HE-6 (induced by source paper "sfnet learning object aware semantic correspondence"):
  with members:
    - "df net unsupervised joint learning of depth and flow using cross task consistency"
    - "self supervised learning of geometrically stable features through probabilistic introspection"
    - "attentive semantic alignment with offset aware correlation kernels"
    - "end to end weakly supervised semantic alignment"
    - "convolutional neural network architecture for geometric matching"
    - "improving landmark localization with semi supervised learning"
    - "dctm discrete continuous transformation matching for semantic flow"
    - "fcss fully convolutional self similarity for dense semantic correspondence"

Connected through hyperedge HE-7 (induced by source paper "slim densepose thrifty learning from sparse annotations and motion cues"):
  with members:
    - "learning category specific mesh reconstruction from image collections"
    - "densereg fully convolutional dense shape regression in the wild"
    - "unsupervised discovery of object landmarks as structural representations"
    - "supervision by registration an unsupervised approach to improve the precision of facial landmark detectors"
    - "densepose dense human pose estimation in the wild"
    - "unsupervised learning of object frames by dense equivariant image labelling"
    - "mass displacement networks"
    - "end to end recovery of human shape and pose"

Connected through hyperedge HE-8 (induced by source paper "pcrnet point cloud registration network using pointnet encoding"):
  with members:
    - "pointnetlk robust efficient point cloud registration using pointnet"
    - "3dfeat net weakly supervised local 3d features for point cloud registration"
    - "fast and accurate point cloud registration using trees of gaussian mixtures"
    - "matching rgb images to cad models for object pose estimation"
    - "iterative transformer network for 3d point cloud"
    - "dynamic graph cnn for learning on point clouds"
    - "pcn point completion network"
    - "pointnetvlad deep point cloud based retrieval for large scale place recognition"

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)