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: "fine grained visual categorization via multi stage metric learning"
  Abstract: Fine-grained visual categorization (FGVC) is to categorize objects into subordinate classes instead of basic classes. One major challenge in FGVC is the co-occurrence of two issues: 1) many subordinate classes are highly correlated and are difficult to distinguish, and 2) there exists the large intra-class variation (e.g., due to object pose). This paper proposes to explicitly address the above two issues via distance metric learning (DML). DML addresses the first issue by learning an embedding so that data points from the same class will be pulled together while those from different classes should be pushed apart from each other; and it addresses the second issue by allowing the flexibility that only a portion of the neighbors (not all data points) from the same class need to be pulled together. However, feature representation of an image is often high dimensional, and DML is known to have difficulty in dealing with high dimensional feature vectors since it would require $\mathcal{O}(d^2)$ for storage and $\mathcal{O}(d^3)$ for optimization. To this end, we proposed a multi-stage metric learning framework that divides the large-scale high dimensional learning problem to a series of simple subproblems, achieving $\mathcal{O}(d)$ computational complexity. The empirical study with FVGC benchmark datasets verifies that our method is both effective and efficient compared to the state-of-the-art FGVC approaches.

Connected through hyperedge HE-1 (induced by source paper "fine grained visual textual representation learning"):
  with members:
    - "one shot fine grained instance retrieval"
    - "fine grained image classification via combining vision and language"
    - "low rank bilinear pooling for fine grained classification"
    - "learning deep representations of fine grained visual descriptions"
    - "generative adversarial text to image synthesis"
    - "weakly supervised fine grained categorization with part based image representation"
    - "hierarchical question image co attention for visual question answering"
    - "fine grained categorization and dataset bootstrapping using deep metric learning with humans in the loop"

Connected through hyperedge HE-2 (induced by source paper "growing a brain fine tuning by increasing model capacity"):
  with members:
    - "knowledge transfer in deep block modular neural networks"
    - "progressive neural networks"
    - "a deep hierarchical approach to lifelong learning in minecraft"
    - "a growing long term episodic semantic memory"
    - "one shot learning with memory augmented neural networks"
    - "learning feed forward one shot learners"
    - "cross stitch networks for multi task learning"
    - "what makes imagenet good for transfer learning"

Connected through hyperedge HE-3 (induced by source paper "fine grained recognition accounting for subtle differences between similar classes"):
  with members:
    - "looking for the devil in the details learning trilinear attention sampling network for fine grained image recognition"
    - "weakly supervised complementary parts models for fine grained image classification from the bottom up"
    - "multi attention multi class constraint for fine grained image recognition"
    - "deep texture manifold for ground terrain recognition"
    - "maximum entropy fine grained classification"
    - "learning to navigate for fine grained classification"
    - "rethinking feature distribution for loss functions in image classification"
    - "pairwise confusion for fine grained visual classification"

Connected through hyperedge HE-4 (induced by source paper "a theoretically sound upper bound on the triplet loss for improving the efficiency of deep distance metric learning"):
  with members:
    - "deep randomized ensembles for metric learning"
    - "deep metric learning with hierarchical triplet loss"
    - "normface l 2 hypersphere embedding for face verification"
    - "prototypical networks for few shot learning"
    - "deep metric learning with angular loss"
    - "no fuss distance metric learning using proxies"
    - "smart mining for deep metric learning"
    - "deep metric learning via facility location"

Connected through hyperedge HE-5 (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"
    - "mining on manifolds metric learning without labels"

Connected through hyperedge HE-6 (induced by source paper "semantic granularity metric learning for visual search"):
  with members:
    - "generalization in metric learning should the embedding layer be the embedding layer"
    - "deep randomized ensembles for metric learning"
    - "where and when to look deep siamese attention networks for video based person re identification"
    - "deep metric learning with hierarchical triplet loss"
    - "deep learning based large scale visual recommendation and search for e commerce"
    - "in defense of the triplet loss for person re identification"
    - "local similarity aware deep feature embedding"
    - "hard aware deeply cascaded embedding"

Connected through hyperedge HE-7 (induced by source paper "a simple and effective framework for pairwise deep metric learning"):
  with members:
    - "hybrid attention based decoupled metric learning for zero shot image retrieval"
    - "multi similarity loss with general pair weighting for deep metric learning"
    - "revisiting metric learning for few shot image classification"
    - "deep metric learning with bier boosting independent embeddings robustly"
    - "large scale distance metric learning with uncertainty"
    - "hard aware point to set deep metric for person re identification"
    - "deep metric learning with hierarchical triplet loss"
    - "attention based ensemble for deep metric learning"

Connected through hyperedge HE-8 (induced by source paper "reaps towards better recognition of fine grained images by region attending and part sequencing"):
  with members:
    - "fast fine grained image classification via weakly supervised discriminative localization"
    - "improved bilinear pooling with cnns"
    - "low rank bilinear pooling for fine grained classification"
    - "fully convolutional attention localization networks efficient attention localization for fine grained recognition"
    - "mining discriminative triplets of patches for fine grained classification"
    - "compact bilinear pooling"
    - "bilinear cnn models for fine grained visual recognition"
    - "spatial transformer networks"

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)