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: "gaussian quadrature for kernel features"
  Abstract: Kernel methods have recently attracted resurgent interest, showing performance competitive with deep neural networks in tasks such as speech recognition. The random Fourier features map is a technique commonly used to scale up kernel machines, but employing the randomized feature map means that $O(\epsilon^{-2})$ samples are required to achieve an approximation error of at most $\epsilon$. We investigate some alternative schemes for constructing feature maps that are deterministic, rather than random, by approximating the kernel in the frequency domain using Gaussian quadrature. We show that deterministic feature maps can be constructed, for any $\gamma > 0$, to achieve error $\epsilon$ with $O(e^{e^\gamma} + \epsilon^{-1/\gamma})$ samples as $\epsilon$ goes to 0. Our method works particularly well with sparse ANOVA kernels, which are inspired by the convolutional layer of CNNs. We validate our methods on datasets in different domains, such as MNIST and TIMIT, showing that deterministic features are faster to generate and achieve accuracy comparable to the state-of-the-art kernel methods based on random Fourier features.

Connected through hyperedge HE-1 (induced by source paper "fast estimation of information theoretic learning descriptors using explicit inner product spaces"):
  with members:
    - "no trick treat kernel adaptive filtering using deterministic features"

Connected through hyperedge HE-2 (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"
    - "quadrature based features for kernel approximation"
    - "random fourier features for kernel ridge regression approximation bounds and statistical guarantees"
    - "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-3 (induced by source paper "no trick treat kernel adaptive filtering using deterministic features"):
  with members:
    - "but how does it work in theory linear svm with random features"
    - "online distributed learning over networks in rkh spaces using random fourier features"
    - "on the error of random fourier features"
    - "explicit approximations of the gaussian kernel"

Connected through hyperedge HE-4 (induced by source paper "sparse recovery for orthogonal polynomial transforms"):
  with members:
    - "sparse reconstruction from hadamard matrices a lower bound"
    - "improved lower bounds for the restricted isometry property of subsampled fourier matrices"
    - "dimension independent sparse fourier transform"
    - "nearly optimal sparse fourier transform"

Connected through hyperedge HE-5 (induced by source paper "nips not even wrong a systematic review of empirically complete demonstrations of algorithmic effectiveness in the machine learning and artificial intelligence literature"):
  with members:
    - "deep sets"
    - "saliency based sequential image attention with multiset prediction"
    - "analyzing hidden representations in end to end automatic speech recognition systems"
    - "deep supervised discrete hashing"
    - "self normalizing neural networks"
    - "batch renormalization towards reducing minibatch dependence in batch normalized models"
    - "structured generative adversarial networks"
    - "first order adaptive sample size methods to reduce complexity of empirical risk minimization"

Connected through hyperedge HE-6 (induced by source paper "low precision random fourier features for memory constrained kernel approximation"):
  with members:
    - "high accuracy low precision training"
    - "large scale kernel learning using block coordinate descent"
    - "orthogonal random features"
    - "recursive sampling for the nystr om method"
    - "on the error of random fourier features"
    - "deep learning with limited numerical precision"
    - "deep fried convnets"
    - "revisiting the nystrom method for improved large scale machine learning"

Connected through hyperedge HE-7 (induced by source paper "quadrature based features for kernel approximation"):
  with members:
    - "recycling randomness with structure for sublinear time kernel expansions"
    - "on the equivalence between kernel quadrature rules and random feature expansions"
    - "on the error of random fourier features"
    - "fastfood approximate kernel expansions in loglinear time"

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)