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 classical algorithms for principal component analysis and supervised clustering"
  Abstract: We describe classical analogues to quantum algorithms for principal component analysis and nearest-centroid clustering. Given sampling assumptions, our classical algorithms run in time polylogarithmic in input, matching the runtime of the quantum algorithms with only polynomial slowdown. These algorithms are evidence that their corresponding problems do not yield exponential quantum speedups. To build our classical algorithms, we use the same techniques as applied in our previous work dequantizing a quantum recommendation systems algorithm. Thus, we provide further evidence for the strength of classical $\ell^2$-norm sampling assumptions when replacing quantum state preparation assumptions, in the machine learning domain.

Connected through hyperedge HE-1 (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 low rank stochastic regression with logarithmic dependence on the dimension"
    - "quantum inspired sublinear classical algorithms for solving low rank linear systems"
    - "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-2 (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 low rank stochastic regression with logarithmic dependence on the dimension"
    - "quantum inspired sublinear classical algorithms for solving low rank linear systems"
    - "lower bounds on the size of semidefinite programming relaxations"

Connected through hyperedge HE-3 (induced by source paper "entanglement and unsorted database search in noise based logic"):
  with members:
    - "a quantum inspired classical algorithm for recommendation systems"
    - "noise based logic gates by operations on the reference system"
    - "drawing from hats by noise based logic"
    - "brain biological noise based logic"
    - "bird s eye view on noise based logic"
    - "noise based logic why noise a comparative study of the necessity of randomness out of orthogonality"
    - "computation using noise based logic efficient string verification over a slow communication channel"
    - "new noise based logic representations to avoid some problems with time complexity"

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 low rank stochastic regression with logarithmic dependence on the dimension"
    - "quantum inspired sublinear classical algorithms for solving low rank linear systems"
    - "polynomial learning of distribution families"

Connected through hyperedge HE-5 (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 low rank stochastic regression with logarithmic dependence on the dimension"
    - "quantum inspired sublinear classical algorithms for solving low rank linear systems"
    - "a kernel method for canonical correlation analysis"

Connected through hyperedge HE-6 (induced by source paper "quantum inspired classical algorithms for singular value transformation"):
  with members:
    - "a quantum inspired classical algorithm for recommendation systems"
    - "quantum inspired low rank stochastic regression with logarithmic dependence on the dimension"
    - "quantum inspired sublinear classical algorithms for solving low rank linear systems"

Connected through hyperedge HE-7 (induced by source paper "quantum inspired support vector machine"):
  with members:
    - "a quantum inspired classical algorithm for recommendation systems"
    - "quantum inspired low rank stochastic regression with logarithmic dependence on the dimension"
    - "quantum inspired sublinear classical algorithms for solving low rank linear systems"

Connected through hyperedge HE-8 (induced by source paper "quantum inspired sublinear classical algorithms for solving low rank linear systems"):
  with members:
    - "a quantum inspired classical algorithm for recommendation systems"
    - "on solving linear systems in sublinear time"
    - "powers of tensors and fast matrix multiplication"

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)