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: "dimension independent similarity computation"
  Abstract: We present a suite of algorithms for Dimension Independent Similarity Computation (DISCO) to compute all pairwise similarities between very high dimensional sparse vectors. All of our results are provably independent of dimension, meaning apart from the initial cost of trivially reading in the data, all subsequent operations are independent of the dimension, thus the dimension can be very large. We study Cosine, Dice, Overlap, and the Jaccard similarity measures. For Jaccard similiarity we include an improved version of MinHash. Our results are geared toward the MapReduce framework. We empirically validate our theorems at large scale using data from the social networking site Twitter. At time of writing, our algorithms are live in production at twitter.com.

Connected through hyperedge HE-1 (induced by source paper "when hashes met wedges a distributed algorithm for finding high similarity vectors"):
  with members:
    - "practical and optimal lsh for angular distance"
    - "diamond sampling for approximate maximum all pairs dot product mad search"
    - "the unified logging infrastructure for data analytics at twitter"
    - "complexity measures for map reduce and comparison to parallel computing"
    - "compressed matrix multiplication"
    - "low rank matrix valued chernoff bounds and approximate matrix multiplication"
    - "layered label propagation a multiresolution coordinate free ordering for compressing social networks"
    - "on sparse representations of linear operators and the approximation of matrix products"

Connected through hyperedge HE-2 (induced by source paper "on the complexity of inner product similarity join"):
  with members:
    - "a faster subquadratic algorithm for finding outlier correlations"
    - "practical and optimal lsh for angular distance"
    - "optimal data dependent hashing for approximate near neighbors"
    - "i o efficient similarity join"
    - "probabilistic polynomials and hamming nearest neighbors"
    - "sketching as a tool for numerical linear algebra"
    - "sketching and embedding are equivalent for norms"
    - "the johnson lindenstrauss lemma is optimal for linear dimensionality reduction"

Connected through hyperedge HE-3 (induced by source paper "efficient similarity search in dynamic data streams"):
  with members:
    - "bottom k and priority sampling set similarity and subset sums with minimal independence"

Connected through hyperedge HE-4 (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"
    - "explicit correlation amplifiers for finding outlier correlations in deterministic subquadratic time"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "dimension independent matrix square using mapreduce"):
  with members:
    - "direct qr factorizations for tall and skinny matrices in mapreduce architectures"
    - "low rank approximation and regression in input sparsity time"
    - "complexity measures for map reduce and comparison to parallel computing"
    - "fast approximation of matrix coherence and statistical leverage"
    - "faster least squares approximation"

Connected through hyperedge HE-6 (induced by source paper "complexity measures for map reduce and comparison to parallel computing"):
  with members:
    - "hadoop performance models"

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)