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: "analysis and extension of arc cosine kernels for large margin classification"
  Abstract: We investigate a recently proposed family of positive-denite kernels that mimic the computation in large neural networks. We examine the properties of these kernels using tools from dierential geometry; specically, we analyze the geometry of surfaces in Hilbert space that are induced by these kernels. When this geometry is described by a Riemannian manifold, we derive results for the metric, curvature, and volume element. Interestingly, though, we nd that the simplest kernel in this family does not admit such an interpretation. We explore two variations of these kernels that mimic computation in neural networks with dierent activation functions. We experiment with these new

Connected through hyperedge HE-1 (induced by source paper "on universal approximation by neural networks with uniform guarantees on approximation of infinite dimensional maps"):
  with members:
    - "neural processes"
    - "the expressive power of neural networks a view from the width"
    - "learning infinite layer networks without the kernel trick"
    - "steps toward deep kernel methods from infinite neural networks"

Connected through hyperedge HE-2 (induced by source paper "additive function approximation in the brain"):
  with members:
    - "but how does it work in theory linear svm with random features"
    - "on the equivalence between kernel quadrature rules and random feature expansions"
    - "on the error of random fourier features"
    - "breaking the curse of dimensionality with convex neural networks"

Connected through hyperedge HE-3 (induced by source paper "a dual certificates analysis of compressive off the grid recovery"):
  with members:
    - "recovery of sparse positive signals on the sphere from low resolution measurements"
    - "breaking the curse of dimensionality with convex neural networks"
    - "breaking the coherence barrier a new theory for compressed sensing"
    - "support detection in super resolution"
    - "towards a mathematical theory of super resolution"
    - "a probabilistic and ripless theory of compressed sensing"

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)