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: "revisiting random binning features fast convergence and strong parallelizability"
  Abstract: Kernel method has been developed as one of the standard approaches for nonlinear learning, which however, does not scale to large data set due to its quadratic complexity in the number of samples. A number of kernel approximation methods have thus been proposed in the recent years, among which the random features method gains much popularity due to its simplicity and direct reduction of nonlinear problem to a linear one. The Random Binning (RB) feature, proposed in the first random-feature paper \cite{rahimi2007random}, has drawn much less attention than the Random Fourier (RF) feature. In this work, we observe that the RB features, with right choice of optimization solver, could be orders-of-magnitude more efficient than other random features and kernel approximation methods under the same requirement of accuracy. We thus propose the first analysis of RB from the perspective of optimization, which by interpreting RB as a Randomized Block Coordinate Descent in the infinite-dimensional space, gives a faster convergence rate compared to that of other random features. In particular, we show that by drawing $R$ random grids with at least $\kappa$ number of non-empty bins per grid in expectation, RB method achieves a convergence rate of $O(1/(\kappa R))$, which not only sharpens its $O(1/\sqrt{R})$ rate from Monte Carlo analysis, but also shows a $\kappa$ times speedup over other random features under the same analysis framework. In addition, we demonstrate another advantage of RB in the L1-regularized setting, where unlike other random features, a RB-based Coordinate Descent solver can be parallelized with guaranteed speedup proportional to $\kappa$. Our extensive experiments demonstrate the superior performance of the RB features over other random features and kernel approximation methods. Our code and data is available at { \url{this https URL}}.

Connected through hyperedge HE-1 (induced by source paper "doctor of crosswise reducing over parametrization in neural networks"):
  with members:
    - "efficient interactive annotation of segmentation datasets with polygon rnn"
    - "autoaugment learning augmentation policies from data"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "training deep networks with synthetic data bridging the reality gap by domain randomization"
    - "mckernel approximate kernel expansions in log linear time through randomization"
    - "progressive growing of gans for improved quality stability and variation"
    - "high resolution deep convolutional generative adversarial networks"
    - "mask r cnn"

Connected through hyperedge HE-2 (induced by source paper "scalable global alignment graph kernel using random features from node embedding to graph embedding"):
  with members:
    - "ddgk learning graph representations for deep divergence graph kernels"
    - "scalable spectral clustering using random binning features"
    - "random warping series a random features method for time series embedding"
    - "sparse diffusion convolutional neural networks"
    - "dgcnn disordered graph convolutional neural network based on the gaussian mixture model"
    - "learning convolutional neural networks for graphs"
    - "primme_svds a high performance preconditioned svd solver for accurate large scale computations"
    - "on valid optimal assignment kernels and applications to graph classification"

Connected through hyperedge HE-3 (induced by source paper "efficient global string kernel with random features beyond counting substructures"):
  with members:
    - "scalable spectral clustering using random binning features"
    - "random warping series a random features method for time series embedding"
    - "word mover s embedding from word2vec to document embedding"
    - "lstm a search space odyssey"
    - "efficient approximation algorithms for string kernel based sequence classification"
    - "adam a method for stochastic optimization"
    - "on the properties of neural machine translation encoder decoder approaches"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"

Connected through hyperedge HE-4 (induced by source paper "kergm kernelized graph matching"):
  with members:
    - "scalable global alignment graph kernel using random features from node embedding to graph embedding"
    - "triangular alignment tame a tensor based approach for higher order network alignment"
    - "stable and informative spectral signatures for graph matching"
    - "subgraph matching kernels for attributed graphs"
    - "a path following algorithm for the graph matching problem"

Connected through hyperedge HE-5 (induced by source paper "scalable spectral clustering using random binning features"):
  with members:
    - "random warping series a random features method for time series embedding"
    - "incremental eigenpair computation for graph laplacian matrices theory and applications"
    - "phase transitions and a model order selection criterion for spectral graph clustering"
    - "primme_svds a high performance preconditioned svd solver for accurate large scale computations"
    - "compressive spectral clustering"
    - "a preconditioned hybrid svd method for accurately computing singular triplets of large matrices"
    - "defining and evaluating network communities based on ground truth"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-6 (induced by source paper "incremental eigenpair computation for graph laplacian matrices theory and applications"):
  with members:
    - "principled multilayer network embedding"
    - "name disambiguation in anonymized graphs using network embedding"
    - "phase transitions and a model order selection criterion for spectral graph clustering"
    - "nous construction and querying of dynamic knowledge graphs"
    - "estimating the trace of the matrix inverse by interpolating from the diagonal of an approximate inverse"
    - "primme_svds a high performance preconditioned svd solver for accurate large scale computations"
    - "trust from the past bayesian personalized ranking based link prediction in knowledge graphs"
    - "spectral detection in the censored block model"

Connected through hyperedge HE-7 (induced by source paper "primme_svds a high performance preconditioned svd solver for accurate large scale computations"):
  with members:
    - "estimating the trace of the matrix inverse by interpolating from the diagonal of an approximate inverse"
    - "a preconditioned hybrid svd method for accurately computing singular triplets of large matrices"

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)