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: "learning fine grained features via a cnn tree for large scale classification"
  Abstract: We propose a novel approach to enhance the discriminability of Convolutional Neural Networks (CNN). The key idea is to build a tree structure that could progressively learn fine-grained features to distinguish a subset of classes, by learning features only among these classes. Such features are expected to be more discriminative, compared to features learned for all the classes. We develop a new algorithm to effectively learn the tree structure from a large number of classes. Experiments on large-scale image classification tasks demonstrate that our method could boost the performance of a given basic CNN model. Our method is quite general, hence it can potentially be used in combination with many other deep learning models.

Connected through hyperedge HE-1 (induced by source paper "deep layered learning in mir"):
  with members:
    - "polyphonic pitch tracking with deep layered learning"
    - "onsets and frames dual objective piano transcription"
    - "towards playlist generation algorithms using rnns trained on within track transitions"
    - "deep residual learning for image recognition"
    - "the application of two level attention models in deep convolutional neural network for fine grained image classification"
    - "the state of the art ten years after a state of the art future research in music information retrieval"
    - "attention for fine grained categorization"
    - "knowledge matters importance of prior information for optimization"

Connected through hyperedge HE-2 (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"
    - "subset feature learning for fine grained category classification"
    - "bilinear cnn models for fine grained visual recognition"
    - "spatial transformer networks"
    - "neural activation constellations unsupervised part model discovery with convolutional networks"

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)