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: "orthogonal random features"
  Abstract: We present an intriguing discovery related to Random Fourier Features: in Gaussian kernel approximation, replacing the random Gaussian matrix by a properly scaled random orthogonal matrix significantly decreases kernel approximation error. We call this technique Orthogonal Random Features (ORF), and provide theoretical and empirical justification for this behavior. Motivated by this discovery, we further propose Structured Orthogonal Random Features (SORF), which uses a class of structured discrete orthogonal matrices to speed up the computation. The method reduces the time cost from $\mathcal{O}(d^2)$ to $\mathcal{O}(d \log d)$, where $d$ is the data dimensionality, with almost no compromise in kernel approximation quality compared to ORF. Experiments on several datasets verify the effectiveness of ORF and SORF over the existing methods. We also provide discussions on using the same type of discrete orthogonal structure for a broader range of applications.

Connected through hyperedge HE-1 (induced by source paper "coke communication censored kernel learning for decentralized non parametric learning"):
  with members:
    - "quantized frank wolfe communication efficient distributed optimization"
    - "online distributed learning over networks in rkh spaces using random fourier features"
    - "sparsified sgd with memory"
    - "random fourier features for kernel ridge regression approximation bounds and statistical guarantees"
    - "communication efficient learning of deep networks from decentralized data"
    - "a diffusion kernel lms algorithm for nonlinear adaptive networks"
    - "on the equivalence between kernel quadrature rules and random feature expansions"
    - "on the error of random fourier features"

Connected through hyperedge HE-2 (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"
    - "quadrature based features for kernel approximation"
    - "random fourier features for kernel ridge regression approximation bounds and statistical guarantees"
    - "a unified optimization view on generalized matching pursuit and frank wolfe"
    - "fastfood approximate kernel expansions in loglinear time"
    - "explicit approximations of the gaussian kernel"

Connected through hyperedge HE-3 (induced by source paper "learning fast algorithms for linear transforms using butterfly factorizations"):
  with members:
    - "neural arithmetic logic units"
    - "quadrature based features for kernel approximation"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "low rank random tensor for bilinear pooling"):
  with members:
    - "ms tcn multi stage temporal convolutional network for action segmentation"
    - "local temporal bilinear pooling for fine grained action parsing"
    - "bilinear attention networks"
    - "hierarchical bilinear pooling for fine grained visual recognition"
    - "higher order pooling of cnn features via kernel linearization for action recognition"
    - "mutan multimodal tucker fusion for visual question answering"
    - "is second order information helpful for large scale visual recognition"
    - "attentional pooling for action recognition"

Connected through hyperedge HE-5 (induced by source paper "sampled softmax with random fourier features"):
  with members:
    - "stochastic negative mining for learning with large output spaces"
    - "normface l 2 hypersphere embedding for face verification"
    - "sphereface deep hypersphere embedding for face recognition"
    - "l2 constrained softmax loss for discriminative face verification"
    - "fast amortized inference and learning in log linear models with randomly perturbed nearest neighbor search"
    - "adaptive sampled softmax with kernel based sampling"
    - "efficient softmax approximation for gpus"
    - "ds mlr exploiting double separability for scaling up distributed multinomial logistic regression"

Connected through hyperedge HE-6 (induced by source paper "learning vector valued functions with local rademacher complexity"):
  with members:
    - "but how does it work in theory linear svm with random features"
    - "a survey on multi task learning"
    - "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-7 (induced by source paper "learning vector valued functions with local rademacher complexity and unlabeled data"):
  with members:
    - "but how does it work in theory linear svm with random features"
    - "data dependent generalization bounds for multi class classification"
    - "a survey on multi task learning"
    - "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-8 (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"
    - "but how does it work in theory linear svm with random features"
    - "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"
    - "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"

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)