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: "generalized min max kernel and generalized consistent weighted sampling"
  Abstract: We propose the "generalized min-max" (GMM) kernel as a measure of data similarity, where data vectors can have both positive and negative entries. GMM is positive definite as there is an associate hashing method named "generalized consistent weighted sampling" (GCWS) which linearizes this (nonlinear) kernel. A natural competitor of GMM is the radial basis function (RBF) kernel, whose corresponding hashing method is known as the "random Fourier features" (RFF). An extensive experimental study on classifications of \textbf{50} publicly available datasets demonstrates that both the GMM and RBF kernels can often substantially improve over linear classifiers. Furthermore, the GCWS hashing method typically requires substantially fewer samples than RFF in order to achieve similar classification accuracies. #R##N#To understand the property of random Fourier features (RFF), we derive the theoretical variance of RFF, which reveals that the variance of RFF has a term that does not vanish at any similarity. In comparison, the variance of GCWS approaches zero at certain similarities. Overall, the relative (to the expectation) variance of RFF is substantially larger than the relative variance of GCWS. This helps explain the superb empirical results of GCWS compared to RFF. #R##N#We expect that GMM and GCWS will be adopted in practice for large-scale statistical machine learning applications and efficient near neighbor search (as GMM generates discrete hash values).

Connected through hyperedge HE-1 (induced by source paper "object proposal with kernelized partial ranking"):
  with members:
    - "what makes for effective detection proposals"
    - "linearized gmm kernels and normalized random fourier features"
    - "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)