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 representation learning and recognition by exploiting hierarchical semantic embedding"
  Abstract: Object categories inherently form a hierarchy with different levels of concept abstraction, especially for fine-grained categories. For example, birds (Aves) can be categorized according to a four-level hierarchy of order, family, genus, and species. This hierarchy encodes rich correlations among various categories across different levels, which can effectively regularize the semantic space and thus make prediction less ambiguous. However, previous studies of fine-grained image recognition primarily focus on categories of one certain level and usually overlook this correlation information. In this work, we investigate simultaneously predicting categories of different levels in the hierarchy and integrating this structured correlation information into the deep neural network by developing a novel Hierarchical Semantic Embedding (HSE) framework. Specifically, the HSE framework sequentially predicts the category score vector of each level in the hierarchy, from highest to lowest. At each level, it incorporates the predicted score vector of the higher level as prior knowledge to learn finer-grained feature representation. During training, the predicted score vector of the higher level is also employed to regularize label prediction by using it as soft targets of corresponding sub-categories. To evaluate the proposed framework, we organize the 200 bird species of the Caltech-UCSD birds dataset with the four-level category hierarchy and construct a large-scale butterfly dataset that also covers four level categories. Extensive experiments on these two and the newly-released VegFru datasets demonstrate the superiority of our HSE framework over the baseline methods and existing competitors.

Connected through hyperedge HE-1 (induced by source paper "channel interaction networks for fine grained image categorization"):
  with members:
    - "looking for the devil in the details learning trilinear attention sampling network for fine grained image recognition"
    - "multi attention multi class constraint for fine grained image recognition"
    - "learning to navigate for fine grained classification"
    - "compact generalized non local network"
    - "hierarchical bilinear pooling for fine grained visual recognition"
    - "non local neural networks"
    - "attention is all you need"
    - "beyond triplet loss a deep quadruplet network for person re identification"

Connected through hyperedge HE-2 (induced by source paper "l relu piece wise linear activation functions for deep fine grained visual categorization"):
  with members:
    - "efficient and accurate estimation of lipschitz constants for deep neural networks"
    - "universal lipschitz approximation in bounded depth neural networks"
    - "multi attention multi class constraint for fine grained image recognition"
    - "sorting out lipschitz function approximation"
    - "lipschitz margin training scalable certification of perturbation invariance for deep neural networks"
    - "l2 nonexpansive neural networks"
    - "self normalizing neural networks"
    - "searching for activation functions"

Connected through hyperedge HE-3 (induced by source paper "knowledge graph transfer network for few shot recognition"):
  with members:
    - "knowledge embedded routing network for scene graph generation"
    - "variational prototyping encoder one shot learning with prototypical images"
    - "learning semantic specific graph representation for multi label image recognition"
    - "knowledge embedded representation learning for fine grained image recognition"
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "deep reasoning with knowledge graph for social relationship understanding"
    - "hybrid knowledge routed modules for large scale object detection"
    - "visual semantic navigation using scene priors"

Connected through hyperedge HE-4 (induced by source paper "adaptive semantic visual tree for hierarchical embeddings"):
  with members:
    - "deepfashion2 a versatile benchmark for detection pose estimation segmentation and re identification of clothing images"
    - "video person re identification by temporal residual learning"
    - "attribute aware attention model for fine grained representation learning"
    - "deep metric learning with bier boosting independent embeddings robustly"
    - "deep randomized ensembles for metric learning"
    - "improving the annotation of deepfashion images for fine grained attribute recognition"
    - "deep metric learning with hierarchical triplet loss"
    - "beyond triplet loss a deep quadruplet network for person re identification"

Connected through hyperedge HE-5 (induced by source paper "semantic bilinear pooling for fine grained recognition"):
  with members:
    - "monet moments embedding network"
    - "improved bilinear pooling with cnns"
    - "towards faster training of global covariance pooling networks by iterative matrix square root normalization"
    - "low rank bilinear pooling for fine grained classification"
    - "fully convolutional attention localization networks efficient attention localization for fine grained recognition"
    - "learning a discriminative filter bank within a cnn for fine grained recognition"
    - "fine grained image classification by exploring bipartite graph labels"
    - "compact bilinear pooling"

Connected through hyperedge HE-6 (induced by source paper "neural task planning with and or graph representations"):
  with members:
    - "learning model based planning from scratch"
    - "multi label image recognition by recurrently discovering attentional regions"
    - "recurrent attentional reinforcement learning for multi label image recognition"
    - "knowledge guided recurrent neural network learning for task oriented action prediction"
    - "diversified visual attention networks for fine grained object classification"
    - "visual semantic planning using deep successor representations"
    - "cognitive mapping and planning for visual navigation"
    - "ai2 thor an interactive 3d environment for visual ai"

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)