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 "explicit correlation amplifiers for finding outlier correlations in deterministic subquadratic time"):
  Abstract: We derandomize G. Valiant's [J. ACM 62 (2015) Art. 13] subquadratic-time algorithm for finding outlier correlations in binary data. Our derandomized algorithm gives deterministic subquadratic scaling essentially for the same parameter range as Valiant's randomized algorithm, but the precise constants we save over quadratic scaling are more modest. Our main technical tool for derandomization is an explicit family of correlation amplifiers built via a family of zigzag-product expanders in Reingold, Vadhan, and Wigderson [Ann. of Math. 155 (2002) 157--187]. We say that a function $f:\{-1,1\}^d\rightarrow\{-1,1\}^D$ is a correlation amplifier with threshold $0\leq\tau\leq 1$, error $\gamma\geq 1$, and strength $p$ an even positive integer if for all pairs of vectors $x,y\in\{-1,1\}^d$ it holds that (i) $|\langle x,y\rangle|<\tau d$ implies $|\langle f(x),f(y)\rangle|\leq(\tau\gamma)^pD$; and (ii) $|\langle x,y\rangle|\geq\tau d$ implies $\bigl(\frac{\langle x,y\rangle}{\gamma d}\bigr)^pD #R##N#\leq\langle f(x),f(y)\rangle\leq #R##N#\bigl(\frac{\gamma\langle x,y\rangle}{d}\bigr)^pD$.

Includes members:
  Member "optimal hashing based time space trade offs for approximate near neighbors":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on the i o complexity of the k nearest neighbor problem")
  Member "polynomial representations of threshold functions and algorithmic applications":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "approximating text to pattern hamming distances")
  Member "scalability and total recall with fast coveringlsh":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "optimal las vegas approximate near neighbors in ell_p")
  Member "pseudorandomness via the discrete fourier transform":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "fourier bounds and pseudorandom generators for product tests")
  Member "a faster subquadratic algorithm for finding outlier correlations":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "subsets and supermajorities unifying hashing based set similarity search")
  Member "locality sensitive hashing without false negatives":
    also connected through other hyperedges:
      - Hyperedge HE-6 (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-7 (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-8 (induced by source paper "algorithms for similarity search and pseudorandomness")

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)