human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "a faster subquadratic algorithm for finding outlier correlations"):
  Abstract: We study the problem of detecting outlier pairs of strongly correlated variables among a collection of $n$ variables with otherwise weak pairwise correlations. After normalization, this task amounts to the geometric task where we are given as input a set of $n$ vectors with unit Euclidean norm and dimension $d$, and for some constants $0<\tau<\rho<1$, we are asked to find all the outlier pairs of vectors whose inner product is at least $\rho$ in absolute value, subject to the promise that all but at most $q$ pairs of vectors have inner product at most $\tau$ in absolute value. #R##N#Improving on an algorithm of G. Valiant [FOCS 2012; J. ACM 2015], we present a randomized algorithm that for Boolean inputs ($\{-1,1\}$-valued data normalized to unit Euclidean length) runs in time \[ \tilde O\bigl(n^{\max\,\{1-\gamma+M(\Delta\gamma,\gamma),\,M(1-\gamma,2\Delta\gamma)\}}+qdn^{2\gamma}\bigr)\,, \] where $0<\gamma<1$ is a constant tradeoff parameter and $M(\mu,\nu)$ is the exponent to multiply an $\lfloor n^\mu\rfloor\times\lfloor n^\nu\rfloor$ matrix with an $\lfloor n^\nu\rfloor\times \lfloor n^\mu\rfloor$ matrix and $\Delta=1/(1-\log_\tau\rho)$. As corollaries we obtain randomized algorithms that run in time \[ \tilde O\bigl(n^{\frac{2\omega}{3-\log_\tau\rho}}+qdn^{\frac{2(1-\log_\tau\rho)}{3-\log_\tau\rho}}\bigr) \] and in time \[ \tilde O\bigl(n^{\frac{4}{2+\alpha(1-\log_\tau\rho)}}+qdn^{\frac{2\alpha(1-\log_\tau\rho)}{2+\alpha(1-\log_\tau\rho)}}\bigr)\,, \] where $2\leq\omega<2.38$ is the exponent for square matrix multiplication and $0.3<\alpha\leq 1$ is the exponent for rectangular matrix multiplication. The notation $\tilde O(\cdot)$ hides polylogarithmic factors in $n$ and $d$ whose degree may depend on $\rho$ and $\tau$. We present further corollaries for the light bulb problem and for learning sparse Boolean functions.

Includes members:
  Member "generating families of practical fast matrix multiplication algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "strassen s 2x2 matrix multiplication algorithm a conceptual perspective")
  Member "on the complexity of inner product similarity join":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "lsf join locality sensitive filtering for distributed all pairs set similarity under skew")
  Member "explicit correlation amplifiers for finding outlier correlations in deterministic subquadratic time":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a faster algorithm enumerating relevant features over finite fields")
  Member "practical and optimal lsh for angular distance":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "differentiable reasoning over a virtual knowledge base")
  Member "locality sensitive hashing without false negatives":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "similarity problems in high dimensions")
  Member "optimal data dependent hashing for approximate near neighbors":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "autoblock a hands off blocking framework for entity matching")
  Member "probabilistic polynomials and hamming nearest neighbors":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "algorithms for similarity search and pseudorandomness")
  Member "a framework for practical parallel fast matrix multiplication":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deterministic combinatorial replacement paths and distance sensitivity oracles")

Question: Please tell me which class the source paper 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)