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: "quantum inspired support vector machine"
  Abstract: Support vector machine (SVM) is a particularly powerful and flexible supervised learning model that analyze data for both classification and regression, whose usual algorithm complexity scales polynomially with the dimension of data space and the number of data points. Inspired by quantum SVM, we present a quantum-inspired classical algorithm for SVM using fast sampling techniques. In our approach, we developed a method sampling kernel matrix by the given information on data points and make classification through estimation of classification expression. Our approach can be applied to various types of SVM, such as linear SVM, non-linear SVM and soft SVM. Theoretical analysis shows one can make classification with arbitrary success probability in logarithmic runtime of both the dimension of data space and the number of data points, matching the runtime of the quantum SVM.

Connected through hyperedge HE-1 (induced by source paper "sampling based sublinear low rank matrix arithmetic framework for dequantizing quantum machine learning"):
  with members:
    - "a quantum inspired classical algorithm for recommendation systems"
    - "a quantum inspired algorithm for general minimum conical hull problems"
    - "quantum inspired classical sublinear time algorithm for solving low rank semidefinite programming via sampling approaches"
    - "a quantum inspired classical algorithm for separable non negative matrix factorization"
    - "quantum inspired low rank stochastic regression with logarithmic dependence on the dimension"
    - "quantum inspired sublinear classical algorithms for solving low rank linear systems"
    - "quantum inspired classical algorithms for principal component analysis and supervised clustering"
    - "lower bounds on the size of semidefinite programming relaxations"

Connected through hyperedge HE-2 (induced by source paper "a quantum inspired algorithm for general minimum conical hull problems"):
  with members:
    - "a quantum inspired classical algorithm for recommendation systems"
    - "quantum inspired classical sublinear time algorithm for solving low rank semidefinite programming via sampling approaches"
    - "quantum inspired low rank stochastic regression with logarithmic dependence on the dimension"
    - "quantum inspired sublinear classical algorithms for solving low rank linear systems"
    - "quantum inspired classical algorithms for principal component analysis and supervised clustering"
    - "polynomial learning of distribution families"

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)