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: "curvature based comparison of two neural networks"
  Abstract: In this paper we show the similarities and differences of two deep neural networks by comparing the manifolds composed of activation vectors in each fully connected layer of them. The main contribution of this paper includes 1) a new data generating algorithm which is crucial for determining the dimension of manifolds; 2) a systematic strategy to compare manifolds. Especially, we take Riemann curvature and sectional curvature as part of criterion, which can reflect the intrinsic geometric properties of manifolds. Some interesting results and phenomenon are given, which help in specifying the similarities and differences between the features extracted by two networks and demystifying the intrinsic mechanism of deep neural networks.

Connected through hyperedge HE-1 (induced by source paper "intrinsic dimension of data representations in deep neural networks"):
  with members:
    - "mechanisms of dimensionality reduction and decorrelation in deep neural networks"
    - "towards understanding the role of over parametrization in generalization of neural networks"
    - "dimensionality driven learning with noisy labels"
    - "characterization of visual object representations in rat primary visual cortex"
    - "emergence of invariance and disentanglement in deep representations"
    - "opening the black box of deep neural networks via information"
    - "understanding deep learning requires rethinking generalization"
    - "efficient representation of low dimensional manifolds using deep networks"

Connected through hyperedge HE-2 (induced by source paper "on geometric structure of activation spaces in neural networks"):
  with members:
    - "on generalization error bounds of noisy gradient methods for non convex learning"
    - "communitygan community detection with generative adversarial nets"
    - "acekg a large scale knowledge graph for academic data mining"
    - "towards understanding learning representations to what extent do different neural networks learn the same representation"
    - "computing the approximate convex hull in high dimensions"
    - "deep learning in neural networks"
    - "deepwalk online learning of social representations"
    - "approximating the volume of unions and intersections of high dimensional geometric objects"

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.LG(Machine Learning)