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: "homophilic clustering by locally asymmetric geometry"
  Abstract: Clustering is indispensable for data analysis in many scientific disciplines. Detecting clusters from heavy noise remains challenging, particularly for high-dimensional sparse data. Based on graph-theoretic framework, the present paper proposes a novel algorithm to address this issue. The locally asymmetric geometries of neighborhoods between data points result in a directed similarity graph to model the structural connectivity of data points. Performing similarity propagation on this directed graph simply by its adjacency matrix powers leads to an interesting discovery, in the sense that if the in-degrees are ordered by the corresponding sorted out-degrees, they will be self-organized to be homophilic layers according to the different distributions of cluster densities, which is dubbed the Homophilic In-degree figure (the HI figure). With the HI figure, we can easily single out all cores of clusters, identify the boundary between cluster and noise, and visualize the intrinsic structures of clusters. Based on the in-degree homophily, we also develop a simple efficient algorithm of linear space complexity to cluster noisy data. Extensive experiments on toy and real-world scientific data validate the effectiveness of our algorithms.

Connected through hyperedge HE-1 (induced by source paper "curriculum learning for deep generative models with clustering"):
  with members:
    - "pioneer networks progressively growing generative autoencoder"
    - "pros and cons of gan evaluation measures"
    - "gan dissection visualizing and understanding generative adversarial networks"
    - "spectral normalization for generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "a style based generator architecture for generative adversarial networks"
    - "high resolution mammogram synthesis using progressive generative adversarial networks"
    - "which training methods for gans do actually converge"

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)