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: "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 $\bigl(\frac{\langle x,y\rangle}{\gamma d}\bigr)^pD #R##N#\leq\langle f(x),f(y)\rangle\leq #R##N#\bigl(\frac{\gamma\langle x,y\rangle}{d}\bigr)^pD$.

Connected through hyperedge HE-1 (induced by source paper "a faster algorithm enumerating relevant features over finite fields"):
  with members:
    - "an illuminating algorithm for the light bulb problem"
    - "a faster subquadratic algorithm for finding outlier correlations"
    - "powers of tensors and fast matrix multiplication"
    - "analysis of boolean functions"

Connected through hyperedge HE-2 (induced by source paper "set similarity search for skewed data"):
  with members:
    - "distributed pcp theorems for hardness of approximation in p"
    - "embedjoin efficient edit similarity joins via embeddings"
    - "scalable and robust set similarity join"
    - "a framework for similarity search with space time tradeoffs using locality sensitive filtering"
    - "optimal hashing based time space trade offs for approximate near neighbors"
    - "on the complexity of inner product similarity join"
    - "polynomial representations of threshold functions and algorithmic applications"
    - "set similarity search beyond minhash"

Connected through hyperedge HE-3 (induced by source paper "a new algorithm for finding closest pair of vectors"):
  with members:
    - "an illuminating algorithm for the light bulb problem"
    - "fast feasible and unfeasible matrix multiplication"
    - "distributed pcp theorems for hardness of approximation in p"
    - "generating families of practical fast matrix multiplication algorithms"
    - "polynomial representations of threshold functions and algorithmic applications"
    - "a faster subquadratic algorithm for finding outlier correlations"
    - "probabilistic polynomials and hamming nearest neighbors"
    - "a framework for practical parallel fast matrix multiplication"

Connected through hyperedge HE-4 (induced by source paper "optimal las vegas locality sensitive data structures"):
  with members:
    - "parameter free locality sensitive hashing for spherical range reporting"
    - "a framework for similarity search with space time tradeoffs using locality sensitive filtering"
    - "optimal hashing based time space trade offs for approximate near neighbors"
    - "locality sensitive hashing without false negatives for l_p"
    - "distance sensitive bloom filters without false negatives"
    - "scalability and total recall with fast coveringlsh"
    - "practical and optimal lsh for angular distance"
    - "locality sensitive hashing without false negatives"

Connected through hyperedge HE-5 (induced by source paper "lower bounds on time space trade offs for approximate near neighbors"):
  with members:
    - "randomized approximate nearest neighbor search with limited adaptivity"
    - "simple average case lower bounds for approximate near neighbor from isoperimetric inequalities"
    - "exploiting the structure stochastic gradient methods using raw clusters"
    - "scalability and total recall with fast coveringlsh"
    - "learning to hash for indexing big data a survey"
    - "a faster subquadratic algorithm for finding outlier correlations"
    - "tight lower bounds for data dependent locality sensitive hashing"
    - "practical and optimal lsh for angular distance"

Connected through hyperedge HE-6 (induced by source paper "a faster subquadratic algorithm for finding outlier correlations"):
  with members:
    - "generating families of practical fast matrix multiplication algorithms"
    - "on the complexity of inner product similarity join"
    - "practical and optimal lsh for angular distance"
    - "locality sensitive hashing without false negatives"
    - "optimal data dependent hashing for approximate near neighbors"
    - "probabilistic polynomials and hamming nearest neighbors"
    - "a framework for practical parallel fast matrix multiplication"
    - "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)