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: "linear algebra and duality of neural networks"
  Abstract: Bases, mappings, projections and metrics, natural for Neural network training, are introduced. Graph-theoretical interpretation is offered. Non-Gaussianity naturally emerges, even in relatively simple datasets. Training statistics, hierarchies and energies are analyzed, from physics point of view. Duality between observables (for example, pixels) and observations is established. Relationship between exact and numerical solutions is studied. Physics and financial mathematics interpretations of a key problem are offered. Examples support all new concepts.

Connected through hyperedge HE-1 (induced by source paper "duality of width and depth of neural networks"):
  with members:
    - "effect of depth and width on local minima in deep learning"
    - "model compression via distillation and quantization"
    - "universal approximation with quadratic deep networks"
    - "fuzzy logic interpretation of quadratic networks"
    - "the power of deeper networks for expressing natural functions"
    - "toward deeper understanding of neural networks the power of initialization and a dual view on expressivity"
    - "why deep neural networks for function approximation"
    - "xnor net imagenet classification using binary convolutional neural networks"

Connected through hyperedge HE-2 (induced by source paper "generating new pictures in complex datasets with a simple neural network"):
  with members:
    - "it takes only two adversarial generator encoder networks"
    - "batch renormalization towards reducing minibatch dependence in batch normalized models"
    - "do gans actually learn the distribution an empirical study"
    - "nips 2016 tutorial generative adversarial networks"
    - "understanding deep learning requires rethinking generalization"
    - "pixel recurrent neural networks"
    - "symmetries and control in generative neural nets"
    - "towards universal neural nets gibbs machines and ace"

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)