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: "self supervised learning of geometrically stable features through probabilistic introspection"
  Abstract: Self-supervision can dramatically cut back the amount of manually-labelled data required to train deep neural networks. While self-supervision has usually been considered for tasks such as image classification, in this paper we aim at extending it to geometry-oriented tasks such as semantic matching and part detection. We do so by building on several recent ideas in unsupervised landmark detection. Our approach learns dense distinctive visual descriptors from an unlabelled dataset of images using synthetic image transformations. It does so by means of a robust probabilistic formulation that can introspectively determine which image regions are likely to result in stable image matching. We show empirically that a network pre-trained in this manner requires significantly less supervision to learn semantic object parts compared to numerous pre-training alternatives. We also show that the pre-trained representation is excellent for semantic object matching.

Connected through hyperedge HE-1 (induced by source paper "robust 6d object pose estimation by learning rgb d features"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "usip unsupervised stable interest point detection from 3d point clouds"
    - "dpod 6d pose object detector and refiner"
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "densefusion 6d object pose estimation by iterative dense fusion"
    - "explaining the ambiguity of object detection and 6d pose from visual data"
    - "bop benchmark for 6d object pose estimation"
    - "deep object pose estimation for semantic robotic grasping of household objects"

Connected through hyperedge HE-2 (induced by source paper "improving semantic analysis on point clouds via auxiliary supervision of local geometric priors"):
  with members:
    - "geonet deep geodesic networks for point cloud analysis"
    - "geometry aware generation of adversarial and cooperative point clouds"
    - "orthogonal deep neural networks"
    - "learning graph embedding with adversarial training methods"
    - "revisiting self supervised visual representation learning"
    - "part aware fine grained object categorization using weakly supervised part detection network"
    - "so net self organizing network for point cloud analysis"
    - "dynamic graph cnn for learning on point clouds"

Connected through hyperedge HE-3 (induced by source paper "self supervised learning of pretext invariant representations"):
  with members:
    - "scaling and benchmarking self supervised visual representation learning"
    - "aet vs aed unsupervised representation learning by auto encoding transformations rather than data"
    - "s4l self supervised semi supervised learning"
    - "learning video representations using contrastive bidirectional transformer"
    - "contrastive multiview coding"
    - "momentum contrast for unsupervised visual representation learning"
    - "large scale adversarial representation learning"
    - "unsupervised pre training of image features on non curated data"

Connected through hyperedge HE-4 (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"
    - "neighbourhood consensus networks"
    - "attentive semantic alignment with offset aware correlation kernels"
    - "tracking emerges by colorizing videos"

Connected through hyperedge HE-5 (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"
    - "multimodal unsupervised image to image translation"
    - "neighbourhood consensus networks"

Connected through hyperedge HE-6 (induced by source paper "r2d2 repeatable and reliable detector and descriptor"):
  with members:
    - "d2 net a trainable cnn for joint detection and description of local features"
    - "key net keypoint detection by handcrafted and learned cnn filters"
    - "lf net learning local features from images"
    - "from handcrafted to deep local invariant features"
    - "hierarchical metric learning and matching for 2d and 3d geometric correspondences"
    - "local descriptors optimized for average precision"
    - "revisiting oxford and paris large scale image retrieval benchmarking"
    - "inloc indoor visual localization with dense matching and view synthesis"

Connected through hyperedge HE-7 (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"
    - "neighbourhood consensus networks"
    - "hierarchical metric learning and matching for 2d and 3d geometric correspondences"
    - "attentive semantic alignment with offset aware correlation kernels"
    - "darts differentiable architecture search"

Connected through hyperedge HE-8 (induced by source paper "unsupervised learning of landmarks by descriptor vector exchange"):
  with members:
    - "learning landmarks from unaligned data using image translation"
    - "unsupervised learning of object landmarks through conditional image generation"
    - "densereg fully convolutional dense shape regression in the wild"
    - "self supervised learning of a facial attribute embedding from video"
    - "wing loss for robust facial landmark localisation with convolutional neural networks"
    - "unsupervised discovery of object landmarks as structural representations"
    - "voxceleb2 deep speaker recognition"
    - "self supervised feature learning by learning to spot artifacts"

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)