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: "subset feature learning for fine grained category classification"
  Abstract: Fine-grained categorisation has been a challenging problem due to small inter-class variation, large intra-class variation and low number of training images. We propose a learning system which first clusters visually similar classes and then learns deep convolutional neural network features specific to each subset. Experiments on the popular fine-grained Caltech-UCSD bird dataset show that the proposed method outperforms recent fine-grained categorisation methods under the most difficult setting: no bounding boxes are presented at test time. It achieves a mean accuracy of 77.5%, compared to the previous best performance of 73.2%. We also show that progressive transfer learning allows us to first learn domain-generic features (for bird classification) which can then be adapted to specific set of bird classes, yielding improvements in accuracy.

Connected through hyperedge HE-1 (induced by source paper "pay attention to convolution filters towards fast and accurate fine grained transfer learning"):
  with members:
    - "idk cascades fast deep learning by learning not to overthink"
    - "fully convolutional attention localization networks efficient attention localization for fine grained recognition"
    - "what makes imagenet good for transfer learning"
    - "pruning filters for efficient convnets"
    - "deep residual learning for image recognition"
    - "structured pruning of deep convolutional neural networks"
    - "rethinking the inception architecture for computer vision"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-2 (induced by source paper "one shot fine grained instance retrieval"):
  with members:
    - "faster r cnn features for instance search"
    - "deep image retrieval learning global representations for image search"
    - "localizing by describing attribute guided attention localization for fine grained recognition"
    - "mask cnn localizing parts and selecting descriptors for fine grained image recognition"
    - "a large scale car dataset for fine grained categorization and verification"
    - "compact bilinear pooling"
    - "bilinear cnn models for fine grained visual recognition"
    - "spatial transformer networks"

Connected through hyperedge HE-3 (induced by source paper "objects as context for detecting their semantic parts"):
  with members:
    - "learning semantic part based models from google images"
    - "joint multi person pose estimation and semantic part segmentation"
    - "semantic object parsing with graph lstm"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "human pose estimation via convolutional part heatmap regression"
    - "do semantic parts emerge in convolutional neural networks"
    - "joint object and part segmentation using deep learned potentials"
    - "neural activation constellations unsupervised part model discovery with convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "diversified visual attention networks for fine grained object classification"):
  with members:
    - "hcp a flexible cnn framework for multi label image classification"
    - "fully convolutional attention localization networks efficient attention localization for fine grained recognition"
    - "simultaneous food localization and recognition"
    - "a large scale car dataset for fine grained categorization and verification"
    - "bilinear cnn models for fine grained visual recognition"
    - "spatial transformer networks"
    - "neural activation constellations unsupervised part model discovery with convolutional networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

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)