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: "cross x learning for fine grained visual categorization"
  Abstract: Recognizing objects from subcategories with very subtle differences remains a challenging task due to the large intra-class and small inter-class variation. Recent work tackles this problem in a weakly-supervised manner: object parts are first detected and the corresponding part-specific features are extracted for fine-grained classification. However, these methods typically treat the part-specific features of each image in isolation while neglecting their relationships between different images. In this paper, we propose Cross-X learning, a simple yet effective approach that exploits the relationships between different images and between different network layers for robust multi-scale feature learning. Our approach involves two novel components: (i) a cross-category cross-semantic regularizer that guides the extracted features to represent semantic parts and, (ii) a cross-layer regularizer that improves the robustness of multi-scale features by matching the prediction distribution across multiple layers. Our approach can be easily trained end-to-end and is scalable to large datasets like NABirds. We empirically analyze the contributions of different components of our approach and demonstrate its robustness, effectiveness and state-of-the-art performance on five benchmark datasets. Code is available at \url{this https URL}.

Connected through hyperedge HE-1 (induced by source paper "fine grained visual classification via progressive multi granularity training of jigsaw patches"):
  with members:
    - "the devil is in the channels mutual channel loss for fine grained image classification"
    - "singan learning a generative model from a single natural image"
    - "weakly supervised complementary parts models for fine grained image classification from the bottom up"
    - "learning to navigate for fine grained classification"
    - "iterative reorganization with weak spatial constraints solving arbitrary jigsaw puzzles for unsupervised representation learning"
    - "a fully progressive approach to single image super resolution"
    - "a style based generator architecture for generative adversarial networks"
    - "progressive growing of gans for improved quality stability and variation"

Connected through hyperedge HE-2 (induced by source paper "measuring dataset granularity"):
  with members:
    - "class balanced loss based on effective number of samples"
    - "measuring the intrinsic dimension of objective landscapes"
    - "large scale fine grained categorization and domain specific transfer learning"
    - "do better imagenet models transfer better"
    - "domain adaptive transfer learning with specialist models"
    - "exploring the limits of weakly supervised pretraining"
    - "prototypical networks for few shot learning"
    - "learning to compare relation network for few shot learning"

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)