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: "knowledge embedded representation learning for fine grained image recognition"
  Abstract: Humans can naturally understand an image in depth with the aid of rich knowledge accumulated from daily lives or professions. For example, to achieve fine-grained image recognition (e.g., categorizing hundreds of subordinate categories of birds) usually requires a comprehensive visual concept organization including category labels and part-level attributes. In this work, we investigate how to unify rich professional knowledge with deep neural network architectures and propose a Knowledge-Embedded Representation Learning (KERL) framework for handling the problem of fine-grained image recognition. Specifically, we organize the rich visual concepts in the form of knowledge graph and employ a Gated Graph Neural Network to propagate node message through the graph for generating the knowledge representation. By introducing a novel gated mechanism, our KERL framework incorporates this knowledge representation into the discriminative image feature learning, i.e., implicitly associating the specific attributes with the feature maps. Compared with existing methods of fine-grained image classification, our KERL framework has several appealing properties: i) The embedded high-level knowledge enhances the feature representation, thus facilitating distinguishing the subtle differences among subordinate categories. ii) Our framework can learn feature maps with a meaningful configuration that the highlighted regions finely accord with the nodes (specific attributes) of the knowledge graph. Extensive experiments on the widely used Caltech-UCSD bird dataset demonstrate the superiority of our KERL framework over existing state-of-the-art methods.

Connected through hyperedge HE-1 (induced by source paper "attention convolutional binary neural tree for fine grained visual categorization"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "see better before looking closer weakly supervised data augmentation network for fine grained visual classification"
    - "gcnet non local networks meet squeeze excitation networks and beyond"
    - "looking for the devil in the details learning trilinear attention sampling network for fine grained image recognition"
    - "fine grained classification of cervical cells using morphological and appearance based convolutional neural networks"
    - "maximum entropy fine grained classification"
    - "adaptive neural trees"
    - "learn to pay attention"

Connected through hyperedge HE-2 (induced by source paper "exploring overall contextual information for image captioning in human like cognitive style"):
  with members:
    - "neural baby talk"
    - "regularizing rnns for caption generation by reconstructing the past with the present"
    - "rethinking the form of latent states in image captioning"
    - "learning to parse and translate improves neural machine translation"
    - "multi label image recognition by recurrently discovering attentional regions"
    - "convolutional image captioning"
    - "skeleton key image captioning by skeleton attribute decomposition"
    - "an analysis of scale invariance in object detection snip"

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"
    - "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"
    - "low shot learning from imaginary data"

Connected through hyperedge HE-4 (induced by source paper "learning semantic specific graph representation for multi label image recognition"):
  with members:
    - "knowledge embedded routing network for scene graph generation"
    - "deep reasoning with knowledge graph for social relationship understanding"
    - "multi label image classification via knowledge distillation from weakly supervised detection"
    - "multi label image recognition by recurrently discovering attentional regions"
    - "recurrent attentional reinforcement learning for multi label image recognition"
    - "learning spatial regularization with image level supervisions for multi label image classification"
    - "improving pairwise ranking for multi label image classification"
    - "hadamard product for low rank bilinear pooling"

Connected through hyperedge HE-5 (induced by source paper "fine grained representation learning and recognition by exploiting hierarchical semantic embedding"):
  with members:
    - "crowd counting using deep recurrent spatial aware network"
    - "multi label image recognition by recurrently discovering attentional regions"
    - "recurrent attentional reinforcement learning for multi label image recognition"
    - "diversified visual attention networks for fine grained object classification"
    - "learning a wavelet like auto encoder to accelerate deep neural networks"
    - "fine grained image classification via combining vision and language"
    - "low rank bilinear pooling for fine grained classification"
    - "disc deep image saliency computing via progressive representation learning"

Connected through hyperedge HE-6 (induced by source paper "learning to segment object candidates via recursive neural networks"):
  with members:
    - "deep reasoning with knowledge graph for social relationship understanding"
    - "active self paced learning for cost effective and progressive face identification"
    - "multi label image recognition by recurrently discovering attentional regions"
    - "cross domain visual matching via generalized similarity measure and feature learning"
    - "recurrent attentional reinforcement learning for multi label image recognition"
    - "multiscale combinatorial grouping for image segmentation and object proposal generation"
    - "disc deep image saliency computing via progressive representation learning"
    - "hcp a flexible cnn framework for multi label image classification"

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)