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: "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.

Connected through hyperedge HE-1 (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"
    - "deep group shuffling random walk for person re identification"
    - "deep metric learning with hierarchical triplet loss"
    - "deep unsupervised learning of visual similarities"

Connected through hyperedge HE-2 (induced by source paper "deep metric learning beyond binary supervision"):
  with members:
    - "beyond triplet loss a deep quadruplet network for person re identification"
    - "diverse and accurate image description using a variational auto encoder with an additive gaussian encoding space"
    - "contrastive learning for image captioning"
    - "self supervised learning of pose embeddings from spatiotemporal relations in videos"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "siamese instance search for tracking"
    - "spice semantic propositional image caption evaluation"
    - "deep metric learning via facility location"

Connected through hyperedge HE-3 (induced by source paper "unsupervised embedding learning via invariant and spreading instance feature"):
  with members:
    - "deep clustering for unsupervised learning of visual features"
    - "in defense of the triplet loss for person re identification"
    - "scaling the scattering transform deep hybrid networks"
    - "dynamic label graph matching for unsupervised video re identification"
    - "alignedreid surpassing human level performance in person re identification"
    - "significance of softmax based features in comparison to distance metric learning based features"
    - "no fuss distance metric learning using proxies"
    - "learning spread out local feature descriptors"

Connected through hyperedge HE-4 (induced by source paper "a probabilistic approach for learning embeddings without supervision"):
  with members:
    - "crdoco pixel level domain transfer with cross domain consistency"
    - "hardness aware deep metric learning"
    - "few shot learning with localization in realistic settings"
    - "unsupervised embedding learning via invariant and spreading instance feature"
    - "generalized zero and few shot learning via aligned variational autoencoders"
    - "deep clustering for unsupervised learning of visual features"
    - "adversarial metric learning"
    - "meta transfer learning for few shot learning"

Connected through hyperedge HE-5 (induced by source paper "mic mining interclass characteristics for improved metric learning"):
  with members:
    - "divide and conquer the embedding space for metric learning"
    - "deep metric learning with bier boosting independent embeddings robustly"
    - "boosting self supervised learning via knowledge transfer"
    - "deep clustering for unsupervised learning of visual features"
    - "deep randomized ensembles for metric learning"
    - "cross and learn cross modal self supervision"
    - "deep metric learning with hierarchical triplet loss"
    - "sampling matters in deep embedding learning"

Connected through hyperedge HE-6 (induced by source paper "label propagation for deep semi supervised learning"):
  with members:
    - "deep clustering for unsupervised learning of visual features"
    - "transductive label augmentation for improved deep network learning"
    - "deep co training for semi supervised image recognition"
    - "unsupervised representation learning by predicting image rotations"
    - "dynamic few shot visual learning without forgetting"
    - "low shot learning with large scale diffusion"
    - "transitive invariance for self supervised visual representation learning"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"

Connected through hyperedge HE-7 (induced by source paper "deep manifold embedding for hyperspectral image classification"):
  with members:
    - "diversity in machine learning"
    - "geometric understanding of deep learning"
    - "rethinking feature distribution for loss functions in image classification"
    - "image reconstruction by domain transform manifold learning"
    - "deep class wise hashing semantics preserving hashing via class wise loss"
    - "going deeper with contextual cnn for hyperspectral image classification"
    - "sphereface deep hypersphere embedding for face recognition"
    - "deep metric learning with angular loss"

Connected through hyperedge HE-8 (induced by source paper "an efficient approximate knn graph method for diffusion on image retrieval"):
  with members:
    - "an accurate retrieval through r mac descriptors for landmark recognition"
    - "low shot learning with large scale diffusion"
    - "iterative manifold embedding layer learned by incomplete data for large scale image retrieval"
    - "end to end learning of deep visual representations for image retrieval"
    - "a survey on learning to hash"
    - "efficient diffusion on region manifolds recovering small objects with compact cnn representations"
    - "particular object retrieval with integral max pooling of cnn activations"
    - "hashing for similarity search a survey"

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)