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: "quadrature based features for kernel approximation"
  Abstract: We consider the problem of improving kernel approximation via randomized feature maps. These maps arise as Monte Carlo approximation to integral representations of kernel functions and scale up kernel methods for larger datasets. We propose to use more efficient numerical integration technique to obtain better estimates of the integrals compared to the state-of-the-art methods. Our approach allows the use of information about the integrand to enhance approximation and facilitates fast computations. We derive the convergence behavior and conduct an extensive empirical study that supports our hypothesis.

Connected through hyperedge HE-1 (induced by source paper "generalization guarantees for sparse kernel approximation with entropic optimal features"):
  with members:
    - "learning bounds for greedy approximation with explicit feature maps from multiple kernels"
    - "random fourier features for kernel ridge regression approximation bounds and statistical guarantees"
    - "a unified optimization view on generalized matching pursuit and frank wolfe"
    - "orthogonal random features"
    - "fastfood approximate kernel expansions in loglinear time"
    - "explicit approximations of the gaussian kernel"

Connected through hyperedge HE-2 (induced by source paper "learning fast algorithms for linear transforms using butterfly factorizations"):
  with members:
    - "neural arithmetic logic units"
    - "learning compressed transforms with low displacement rank"
    - "c ir cnn accelerating and compressing deep neural networks using block circulant weight matrices"
    - "a semantic loss function for deep learning with symbolic knowledge"
    - "strassennets deep learning with a multiplication budget"
    - "hyperband a novel bandit based approach to hyperparameter optimization"
    - "neural combinatorial optimization with reinforcement learning"
    - "orthogonal random features"

Connected through hyperedge HE-3 (induced by source paper "random fourier features via fast surrogate leverage weighted sampling"):
  with members:
    - "nystrom landmark sampling and regularized christoffel functions"
    - "but how does it work in theory linear svm with random features"
    - "low precision random fourier features for memory constrained kernel approximation"
    - "random fourier features for kernel ridge regression approximation bounds and statistical guarantees"
    - "gaussian quadrature for kernel features"
    - "orthogonal random features"
    - "input sparsity time low rank approximation via ridge leverage score sampling"
    - "on the equivalence between kernel quadrature rules and random feature expansions"

Connected through hyperedge HE-4 (induced by source paper "preconditioning kaczmarz method by sketching"):
  with members:
    - "escaping saddle points with adaptive gradient methods"
    - "random fourier features for kernel ridge regression approximation bounds and statistical guarantees"
    - "frequent directions simple and deterministic matrix sketching"
    - "adam a method for stochastic optimization"

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)