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: "deep unsupervised learning of visual similarities"
  Abstract: Abstract   Exemplar learning of visual similarities in an unsupervised manner is a problem of paramount importance to computer vision. In this context, however, the recent breakthrough in deep learning could not yet unfold its full potential. With only a single positive sample, a great imbalance between one positive and many negatives, and unreliable relationships between most samples, training of Convolutional Neural networks is impaired. In this paper we use weak estimates of local similarities and propose a single optimization problem to extract batches of samples with mutually consistent relations. Conflicting relations are distributed over different batches and similar samples are grouped into compact groups. Learning visual similarities is then framed as a sequence of categorization tasks. The CNN then consolidates transitivity relations within and between groups and learns a single representation for all samples without the need for labels. The proposed unsupervised approach has shown competitive performance on detailed posture analysis and object classification.

Connected through hyperedge HE-1 (induced by source paper "transferring dense pose to proximal animal classes"):
  with members:
    - "semi supervised segmentation of salt bodies in seismic images using an ensemble of convolutional neural networks"
    - "deep learning tools for the measurement of animal behavior in neuroscience"
    - "billion scale semi supervised learning for image classification"
    - "unsupervised part based disentangling of object shape and appearance"
    - "three d safari learning to estimate zebra pose shape and texture from images in the wild"
    - "slim densepose thrifty learning from sparse annotations and motion cues"
    - "c3dpo canonical 3d pose networks for non rigid structure from motion"
    - "unsupervised learning of landmarks by descriptor vector exchange"

Connected through hyperedge HE-2 (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"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "cycada cycle consistent adversarial domain adaptation"
    - "temporal action detection with structured segment networks"

Connected through hyperedge HE-3 (induced by source paper "divide and conquer the embedding space for metric learning"):
  with members:
    - "improving spatiotemporal self supervision by deep reinforcement learning"
    - "learning discriminative features with multiple granularities for person re identification"
    - "deep metric learning with bier boosting independent embeddings robustly"
    - "harmonious attention network for person re identification"
    - "deep randomized ensembles for metric learning"
    - "mining on manifolds metric learning without labels"
    - "deep group shuffling random walk for person re identification"
    - "deep metric learning with hierarchical triplet loss"

Connected through hyperedge HE-4 (induced by source paper "semi supervised segmentation of salt bodies in seismic images using an ensemble of convolutional neural networks"):
  with members:
    - "albumentations fast and flexible image augmentations"
    - "improving spatiotemporal self supervision by deep reinforcement learning"
    - "sfa gtm seismic facies analysis based on generative topographic map and rbf"
    - "automatic salt deposits segmentation a deep learning approach"
    - "ternausnet u net with vgg11 encoder pre trained on imagenet for image segmentation"
    - "concurrent spatial and channel squeeze excitation in fully convolutional networks"
    - "unsupervised representation learning by sorting sequences"
    - "deep unsupervised similarity learning using partially ordered sets"

Connected through hyperedge HE-5 (induced by source paper "unsupervised representation learning by discovering reliable image relations"):
  with members:
    - "deep clustering for unsupervised learning of visual features"
    - "unsupervised representation learning by predicting image rotations"
    - "sampling matters in deep embedding learning"
    - "transitive invariance for self supervised visual representation learning"
    - "colorization as a proxy task for visual understanding"
    - "unsupervised video understanding by reconciliation of posture similarities"
    - "representation learning by learning to count"
    - "adversarial feature learning"

Connected through hyperedge HE-6 (induced by source paper "improving spatiotemporal self supervision by deep reinforcement learning"):
  with members:
    - "boosting self supervised learning via knowledge transfer"
    - "unsupervised representation learning by predicting image rotations"
    - "deeppermnet visual permutation learning"
    - "transitive invariance for self supervised visual representation learning"
    - "self supervised learning of visual features through embedding images into text topic spaces"
    - "unsupervised video understanding by reconciliation of posture similarities"
    - "self supervised learning of pose embeddings from spatiotemporal relations in videos"
    - "unsupervised representation learning by sorting sequences"

Connected through hyperedge HE-7 (induced by source paper "cross and learn cross modal self supervision"):
  with members:
    - "improving spatiotemporal self supervision by deep reinforcement learning"
    - "boosting self supervised learning via knowledge transfer"
    - "colorization as a proxy task for visual understanding"
    - "unsupervised video understanding by reconciliation of posture similarities"
    - "deep multimodal representation learning from temporal data"
    - "the kinetics human action video dataset"
    - "unsupervised representation learning by sorting sequences"
    - "representation learning by learning to count"

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)