human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "mining on manifolds metric learning without labels"):
  Abstract: In this work we present a novel unsupervised framework for hard training example mining. The only input to the method is a collection of images relevant to the target application and a meaningful initial representation, provided e.g. by pre-trained CNN. Positive examples are distant points on a single manifold, while negative examples are nearby points on different manifolds. Both types of examples are revealed by disagreements between Euclidean and manifold similarities. The discovered examples can be used in training with any discriminative loss. The method is applied to unsupervised fine-tuning of pre-trained networks for fine-grained classification and particular object retrieval. Our models are on par or are outperforming prior models that are fully or partially supervised.

Includes members:
  Member "nearest neighbour radial basis function solvers for deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "fast fine grained image classification via weakly supervised discriminative localization")
  Member "polysemous codes":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning to compress and search visual data in large scale systems")
  Member "parametric manifold learning via sparse multidimensional scaling":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning latent space dynamics for tactile servoing")
  Member "sampling matters in deep embedding learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "revisiting training strategies and generalization performance in deep metric learning")
  Member "transitive invariance for self supervised visual representation learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning video object segmentation from unlabeled videos")
  Member "self supervised learning of visual features through embedding images into text topic spaces":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "variational hetero encoder randomized gans for joint image text modeling")
  Member "iterative manifold embedding layer learned by incomplete data for large scale image retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "unsupervised semantic based aggregation of deep convolutional features")
  Member "multi task self supervised visual learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a large scale study of representation learning with the visual task adaptation benchmark")

Question: Please tell me which class the source paper 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)