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: "one shot fine grained instance retrieval"
  Abstract: Fine-Grained Visual Categorization (FGVC) has achieved significant progress recently. However, the number of fine-grained species could be huge and dynamically increasing in real scenarios, making it difficult to recognize unseen objects under the current FGVC framework. This raises an open issue to perform large-scale fine-grained identification without a complete training set. Aiming to conquer this issue, we propose a retrieval task named One-Shot Fine-Grained Instance Retrieval (OSFGIR). "One-Shot" denotes the ability of identifying unseen objects through a fine-grained retrieval task assisted with an incomplete auxiliary training set. This paper first presents the detailed description to OSFGIR task and our collected OSFGIR-378K dataset. Next, we propose the Convolutional and Normalization Networks (CN-Nets) learned on the auxiliary dataset to generate a concise and discriminative representation. Finally, we present a coarse-to-fine retrieval framework consisting of three components, i.e., coarse retrieval, fine-grained retrieval, and query expansion, respectively. The framework progressively retrieves images with similar semantics, and performs fine-grained identification. Experiments show our OSFGIR framework achieves significantly better accuracy and efficiency than existing FGVC and image retrieval methods, thus could be a better solution for large-scale fine-grained object identification.

Connected through hyperedge HE-1 (induced by source paper "fine grained visual textual representation learning"):
  with members:
    - "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"
    - "character level convolutional networks for text classification"

Connected through hyperedge HE-2 (induced by source paper "compare more nuanced pairwise alignment bilinear network for few shot fine grained learning"):
  with members:
    - "piecewise classifier mappings learning fine grained learners for novel categories with few examples"
    - "learning to propagate labels transductive propagation network for few shot learning"
    - "cross modal hallucination for few shot fine grained recognition"
    - "prototypical networks for few shot learning"
    - "learning to compare relation network for few shot learning"
    - "towards faster training of global covariance pooling networks by iterative matrix square root normalization"
    - "the inaturalist species classification and detection dataset"
    - "matching networks for one shot learning"

Connected through hyperedge HE-3 (induced by source paper "style separation and synthesis via generative adversarial networks"):
  with members:
    - "fully convolutional adaptation networks for semantic segmentation"
    - "learning to adapt structured output space for semantic segmentation"
    - "loss sensitive generative adversarial networks on lipschitz densities"
    - "pose guided person image generation"
    - "unsupervised generative adversarial cross modal hashing"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "diversified texture synthesis with feed forward networks"
    - "genegan learning object transfiguration and attribute subspace from unpaired data"

Connected through hyperedge HE-4 (induced by source paper "adversarial learning for fine grained image search"):
  with members:
    - "object part attention model for fine grained image classification"
    - "deep semantic hashing with generative adversarial networks"
    - "binary generative adversarial networks for image retrieval"
    - "fine grained discriminative localization via saliency guided faster r cnn"
    - "convolutional neural network architecture for geometric matching"
    - "visual search at ebay"
    - "unsupervised image to image translation networks"
    - "lr gan layered recursive generative adversarial networks for image generation"

Connected through hyperedge HE-5 (induced by source paper "ram a region aware deep model for vehicle re identification"):
  with members:
    - "learning deep neural networks for vehicle re id with visual spatio temporal path proposals"
    - "glad global local alignment descriptor for pedestrian retrieval"
    - "person transfer gan to bridge domain gap for person re identification"
    - "a large scale car dataset for fine grained categorization and verification"
    - "deep residual learning for image recognition"
    - "facenet a unified embedding for face recognition and clustering"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "embedding label structures for fine grained feature representation"

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)