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: "but how does it work in theory linear svm with random features"
  Abstract: We prove that, under low noise assumptions, the support vector machine with $N\ll m$ random features (RFSVM) can achieve the learning rate faster than $O(1/\sqrt{m})$ on a training set with $m$ samples when an optimized feature map is used. Our work extends the previous fast rate analysis of random features method from least square loss to 0-1 loss. We also show that the reweighted feature selection method, which approximates the optimized feature map, helps improve the performance of RFSVM in experiments on a synthetic data set.

Connected through hyperedge HE-1 (induced by source paper "learning vector valued functions with local rademacher complexity"):
  with members:
    - "a survey on multi task learning"
    - "orthogonal random features"
    - "a vector contraction inequality for rademacher complexities"
    - "an overview of gradient descent optimization algorithms"
    - "multi class svms from tighter data dependent generalization bounds to novel algorithms"
    - "on the equivalence between kernel quadrature rules and random feature expansions"
    - "adam a method for stochastic optimization"
    - "fastfood approximate kernel expansions in loglinear time"

Connected through hyperedge HE-2 (induced by source paper "learning vector valued functions with local rademacher complexity and unlabeled data"):
  with members:
    - "data dependent generalization bounds for multi class classification"
    - "a survey on multi task learning"
    - "orthogonal random features"
    - "a vector contraction inequality for rademacher complexities"
    - "multi class svms from tighter data dependent generalization bounds to novel algorithms"
    - "adam a method for stochastic optimization"
    - "generalized singular value thresholding"
    - "multiclass learnability and the erm principle"

Connected through hyperedge HE-3 (induced by source paper "a general scoring rule for randomized kernel approximation with application to canonical correlation analysis"):
  with members:
    - "on sampling random features from empirical leverage scores implementation and theoretical guarantees"
    - "random fourier features for kernel ridge regression approximation bounds and statistical guarantees"
    - "data driven random fourier features using stein effect"
    - "stochastic approximation for canonical correlation analysis"
    - "orthogonal random features"
    - "efficient globally convergent stochastic optimization for canonical correlation analysis"
    - "on the equivalence between kernel quadrature rules and random feature expansions"
    - "large scale approximate kernel canonical correlation analysis"

Connected through hyperedge HE-4 (induced by source paper "random fourier features via fast surrogate leverage weighted sampling"):
  with members:
    - "nystrom landmark sampling and regularized christoffel functions"
    - "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"
    - "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-5 (induced by source paper "on sampling random features from empirical leverage scores implementation and theoretical guarantees"):
  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"
    - "data driven random fourier features using stein effect"
    - "orthogonal random features"
    - "on the equivalence between kernel quadrature rules and random feature expansions"
    - "fastfood approximate kernel expansions in loglinear time"
    - "random feature maps for dot product kernels"
    - "sharp analysis of low rank kernel matrix approximations"

Connected through hyperedge HE-6 (induced by source paper "efficient data analytics on augmented similarity triplets"):
  with members:
    - "crowdsourced correlation clustering with relative distance comparisons"
    - "structured adaptive and random spinners for fast machine learning computations"
    - "t exponential triplet embedding"
    - "efficient online relative comparison kernel learning"
    - "cost effective hits for relative similarity comparisons"
    - "adaptively learning the crowd kernel"
    - "fast set intersection and two patterns matching"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-7 (induced by source paper "no trick treat kernel adaptive filtering using deterministic features"):
  with members:
    - "online distributed learning over networks in rkh spaces using random fourier features"
    - "gaussian quadrature for kernel features"
    - "on the error of random fourier features"
    - "explicit approximations of the gaussian kernel"

Connected through hyperedge HE-8 (induced by source paper "additive function approximation in the brain"):
  with members:
    - "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"
    - "analysis and extension of arc cosine kernels for large margin classification"

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)