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: "improving image clustering with multiple pretrained cnn feature extractors"
  Abstract: For many image clustering problems, replacing raw image data with features extracted by a pretrained convolutional neural network (CNN), leads to better clustering performance. However, the specific features extracted, and, by extension, the selected CNN architecture, can have a major impact on the clustering results. In practice, this crucial design choice is often decided arbitrarily due to the impossibility of using cross-validation with unsupervised learning problems. However, information contained in the different pretrained CNN architectures may be complementary, even when pretrained on the same data. To improve clustering performance, we rephrase the image clustering problem as a multi-view clustering (MVC) problem that considers multiple different pretrained feature extractors as different "views" of the same data. We then propose a multi-input neural network architecture that is trained end-to-end to solve the MVC problem effectively. Our experimental results, conducted on three different natural image datasets, show that: 1. using multiple pretrained CNNs jointly as feature extractors improves image clustering; 2. using an end-to-end approach improves MVC; and 3. combining both produces state-of-the-art results for the problem of image clustering.

Connected through hyperedge HE-1 (induced by source paper "multidec multi modal clustering of image caption pairs"):
  with members:
    - "cross modal retrieval in the cooking context learning semantic text image embeddings"
    - "deep clustering for unsupervised learning of visual features"
    - "end to end cross modality retrieval with cca projections and pairwise ranking loss"
    - "learning robust visual semantic embeddings"
    - "cnn features are also great at unsupervised classification"
    - "ezlearn exploiting organic supervision in large scale data annotation"
    - "deep clustering via joint convolutional autoencoder embedding and relative entropy minimization"
    - "enriching word vectors with subword information"

Connected through hyperedge HE-2 (induced by source paper "big data clustering k means or k indicators"):
  with members:
    - "deep subspace clustering networks"
    - "infogan interpretable representation learning by information maximizing generative adversarial nets"

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)