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: "linearized gmm kernels and normalized random fourier features"
  Abstract: The method of "random Fourier features (RFF)" has become a popular tool for approximating the "radial basis function (RBF)" kernel. The variance of RFF is actually large. Interestingly, the variance can be substantially reduced by a simple normalization step as we theoretically demonstrate. We name the improved scheme as the "normalized RFF (NRFF)". #R##N#We also propose the "generalized min-max (GMM)" kernel as a measure of data similarity. GMM is positive definite as there is an associated hashing method named "generalized consistent weighted sampling (GCWS)" which linearizes this nonlinear kernel. We provide an extensive empirical evaluation of the RBF kernel and the GMM kernel on more than 50 publicly available datasets. For a majority of the datasets, the (tuning-free) GMM kernel outperforms the best-tuned RBF kernel. #R##N#We conduct extensive experiments for comparing the linearized RBF kernel using NRFF with the linearized GMM kernel using GCWS. We observe that, to reach a comparable classification accuracy, GCWS typically requires substantially fewer samples than NRFF, even on datasets where the original RBF kernel outperforms the original GMM kernel. The empirical success of GCWS (compared to NRFF) can also be explained from a theoretical perspective. Firstly, the relative variance (normalized by the squared expectation) of GCWS is substantially smaller than that of NRFF, except for the very high similarity region (where the variances of both methods are close to zero). Secondly, if we make a model assumption on the data, we can show analytically that GCWS exhibits much smaller variance than NRFF for estimating the same object (e.g., the RBF kernel), except for the very high similarity region.

Connected through hyperedge HE-1 (induced by source paper "b bit sketch trie scalable similarity search on integer sketches"):
  with members:
    - "pigeonring a principle for faster thresholded similarity search"
    - "fast exact search in hamming space with multi index hashing"
    - "from theory to practice plug and play with succinct data structures"
    - "searching in one billion vectors re rank with source coding"
    - "b bit minwise hashing"

Connected through hyperedge HE-2 (induced by source paper "bagminhash minwise hashing algorithm for weighted sets"):
  with members:
    - "topic modeling of public repositories at scale using names in source code"
    - "fast similarity sketching"
    - "optimal densification for fast and accurate minwise hashing"
    - "improved consistent weighted sampling revisited"
    - "superminhash a new minwise hashing algorithm for jaccard similarity estimation"
    - "ssh sketch shingle hash for indexing massive scale time series"
    - "consistent weighted sampling made fast small and easy"
    - "optimal discrete uniform generation from coin flips and applications"

Connected through hyperedge HE-3 (induced by source paper "space efficient feature maps for string alignment kernels"):
  with members:
    - "random warping series a random features method for time series embedding"
    - "embedjoin efficient edit similarity joins via embeddings"
    - "ups and downs modeling the visual evolution of fashion trends with one class collaborative filtering"
    - "orthogonal random features"
    - "image based recommendations on styles and substitutes"
    - "fastfood approximate kernel expansions in loglinear time"
    - "a kernel for time series based on global alignments"

Connected through hyperedge HE-4 (induced by source paper "object proposal with kernelized partial ranking"):
  with members:
    - "generalized min max kernel and generalized consistent weighted sampling"
    - "what makes for effective detection proposals"
    - "spectral mle top k rank aggregation from pairwise comparisons"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "very deep convolutional networks for large scale image recognition"
    - "object proposal generation using two stage cascade svms"
    - "overfeat integrated recognition localization and detection using convolutional networks"
    - "scalable object detection using deep neural networks"

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)