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 low rank stochastic regression with logarithmic dependence on the dimension"
  Abstract: htmlabstractWe construct an efficient classical analogue of the quantum matrix inversion algorithm [HHL09] for low-rank matrices. Inspired by recent work of Tang [Tan18a], assuming length-square sampling access to input data, we implement the pseudo-inverse of a low-rank matrix and sample from the solution to the problem Ax = b using fast sampling techniques. We implement the pseudo-inverse by finding an approximate singular value decomposition of A via subsampling, then inverting the singular values. In principle, the approach can also be used to apply any desired “smooth” function to the singular values. Since many quantum algorithms can be expressed as a singular value transformation problem [GSLW18], our result suggests that more low-rank quantum algorithms can be effectively “dequantised” into classical length-square sampling algorithms.

Connected through hyperedge HE-1 (induced by source paper "robust and sample optimal algorithms for psd low rank approximation"):
  with members:
    - "a quantum inspired classical algorithm for recommendation systems"
    - "sublinear time numerical linear algebra for structured matrices"
    - "sample optimal low rank approximation of distance matrices"
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "quantum inspired sublinear classical algorithms for solving low rank linear systems"
    - "sublinear time low rank approximation of distance matrices"
    - "sublinear time low rank approximation of positive semidefinite matrices"
    - "recursive sampling for the nystr om method"

Connected through hyperedge HE-2 (induced by source paper "quantum inspired classical sublinear time algorithm for solving low rank semidefinite programming via sampling approaches"):
  with members:
    - "a quantum inspired classical algorithm for recommendation systems"
    - "fully dynamic maximal independent set with sublinear update time"
    - "approximate nearest neighbor search in high dimensions"
    - "solving linear programs in the current matrix multiplication time"
    - "quantum inspired sublinear classical algorithms for solving low rank linear systems"
    - "quantum inspired classical algorithms for principal component analysis and supervised clustering"
    - "fully dynamic approximate maximum matching and minimum vertex cover in o log 3 n worst case update time"
    - "dynamic algorithms for graph coloring"

Connected through hyperedge HE-3 (induced by source paper "sampling based sublinear low rank matrix arithmetic framework for dequantizing quantum machine learning"):
  with members:
    - "quantum inspired support vector machine"
    - "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 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-4 (induced by source paper "a quantum inspired algorithm for general minimum conical hull problems"):
  with members:
    - "quantum inspired support vector machine"
    - "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 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"

Connected through hyperedge HE-5 (induced by source paper "a quantum inspired classical algorithm for separable non negative matrix factorization"):
  with members:
    - "a quantum inspired classical algorithm for recommendation systems"
    - "quantum inspired sublinear classical algorithms for solving low rank linear systems"
    - "learning topic models going beyond svd"
    - "computing a nonnegative matrix factorization provably"
    - "on the complexity of nonnegative matrix factorization"

Connected through hyperedge HE-6 (induced by source paper "quantum inspired canonical correlation analysis for exponentially large dimensional data"):
  with members:
    - "a quantum inspired classical algorithm for recommendation systems"
    - "quantum inspired sublinear classical algorithms for solving low rank linear systems"
    - "quantum inspired classical algorithms for principal component analysis and supervised clustering"
    - "a kernel method for canonical correlation analysis"

Connected through hyperedge HE-7 (induced by source paper "quantum inspired classical algorithms for singular value transformation"):
  with members:
    - "a quantum inspired classical algorithm for recommendation systems"
    - "quantum inspired sublinear classical algorithms for solving low rank linear systems"
    - "quantum inspired classical algorithms for principal component analysis and supervised clustering"

Connected through hyperedge HE-8 (induced by source paper "quantum inspired support vector machine"):
  with members:
    - "a quantum inspired classical algorithm for recommendation systems"
    - "quantum inspired sublinear classical algorithms for solving low rank linear systems"
    - "quantum inspired classical algorithms for principal component analysis and supervised clustering"

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.DS(Data Structures and Algorithms)