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: "a quantum inspired classical algorithm for recommendation systems"
  Abstract: We give a classical analogue to Kerenidis and Prakash’s quantum recommendation system, previously believed to be one of the strongest candidates for provably exponential speedups in quantum machine learning. Our main result is an algorithm that, given an m × n matrix in a data structure supporting certain l2-norm sampling operations, outputs an l2-norm sample from a rank-k approximation of that matrix in time O(poly(k)log(mn)), only polynomially slower than the quantum algorithm. As a consequence, Kerenidis and Prakash’s algorithm does not in fact give an exponential speedup over classical algorithms. Further, under strong input assumptions, the classical recommendation system resulting from our algorithm produces recommendations exponentially faster than previous classical systems, which run in time linear in m and n.    The main insight of this work is the use of simple routines to manipulate l2-norm sampling distributions, which play the role of quantum superpositions in the classical setting. This correspondence indicates a potentially fruitful framework for formally comparing quantum machine learning algorithms to classical machine learning algorithms.

Connected through hyperedge HE-1 (induced by source paper "robust and sample optimal algorithms for psd low rank approximation"):
  with members:
    - "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 low rank stochastic regression with logarithmic dependence on the dimension"
    - "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:
    - "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"
    - "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 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-4 (induced by source paper "entanglement and unsorted database search in noise based logic"):
  with members:
    - "noise based logic gates by operations on the reference system"
    - "quantum inspired classical algorithms for principal component analysis and supervised clustering"
    - "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-5 (induced by source paper "aircraft loading optimization memcomputing the 5th airbus problem"):
  with members:
    - "accelerating deep learning with memcomputing"
    - "stress testing memcomputing on hard combinatorial optimization problems"
    - "instantons in self organizing logic gates"
    - "memcomputing integer linear programming"
    - "perspective memcomputing leveraging memory and physics to compute efficiently"
    - "polynomial time solution of prime factorization and np complete problems with digital memcomputing machines"
    - "universal memcomputing machines"
    - "memcomputing np complete problems in polynomial time using polynomial resources and collective states"

Connected through hyperedge HE-6 (induced by source paper "a quantum inspired algorithm for general minimum conical hull problems"):
  with members:
    - "quantum inspired support vector machine"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "a quantum inspired classical algorithm for separable non negative matrix factorization"):
  with members:
    - "quantum inspired low rank stochastic regression with logarithmic dependence on the dimension"
    - "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-8 (induced by source paper "quantum inspired canonical correlation analysis for exponentially large dimensional data"):
  with members:
    - "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"
    - "a kernel method for canonical correlation analysis"

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.IR(Information Retrieval)