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: "scalability and total recall with fast coveringlsh"
  Abstract: Locality-sensitive hashing (LSH) has emerged as the dominant algorithmic technique for similarity search with strong performance guarantees in high-dimensional spaces. A drawback of traditional LSH schemes is that they may have \emph{false negatives}, i.e., the recall is less than 100\%. This limits the applicability of LSH in settings requiring precise performance guarantees. Building on the recent theoretical "CoveringLSH" construction that eliminates false negatives, we propose a fast and practical covering LSH scheme for Hamming space called \emph{Fast CoveringLSH (fcLSH)}. Inheriting the design benefits of CoveringLSH our method avoids false negatives and always reports all near neighbors. Compared to CoveringLSH we achieve an asymptotic improvement to the hash function computation time from $\mathcal{O}(dL)$ to $\mathcal{O}(d + L\log{L})$, where $d$ is the dimensionality of data and $L$ is the number of hash tables. Our experiments on synthetic and real-world data sets demonstrate that \emph{fcLSH} is comparable (and often superior) to traditional hashing-based approaches for search radius up to 20 in high-dimensional Hamming space.

Connected through hyperedge HE-1 (induced by source paper "optimal las vegas approximate near neighbors in ell_p"):
  with members:
    - "approximate nearest neighbor search in high dimensions"
    - "optimal las vegas locality sensitive data structures"
    - "optimal hashing based time space trade offs for approximate near neighbors"
    - "set similarity search beyond minhash"
    - "tight lower bounds for data dependent locality sensitive hashing"
    - "practical and optimal lsh for angular distance"
    - "locality sensitive hashing without false negatives"
    - "optimal approximate matrix product in terms of stable rank"

Connected through hyperedge HE-2 (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"
    - "explicit correlation amplifiers for finding outlier correlations in deterministic subquadratic time"
    - "distance sensitive bloom filters without false negatives"
    - "practical and optimal lsh for angular distance"
    - "locality sensitive hashing without false negatives"

Connected through hyperedge HE-3 (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"
    - "explicit correlation amplifiers for finding outlier correlations in deterministic subquadratic time"
    - "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-4 (induced by source paper "explicit correlation amplifiers for finding outlier correlations in deterministic subquadratic time"):
  with members:
    - "optimal hashing based time space trade offs for approximate near neighbors"
    - "polynomial representations of threshold functions and algorithmic applications"
    - "pseudorandomness via the discrete fourier transform"
    - "a faster subquadratic algorithm for finding outlier correlations"
    - "locality sensitive hashing without false negatives"
    - "optimal data dependent hashing for approximate near neighbors"
    - "probabilistic polynomials and hamming nearest neighbors"
    - "almost optimal pseudorandom generators for spherical caps"

Connected through hyperedge HE-5 (induced by source paper "massively parallel similarity join edge isoperimetry and distance correlations on the hypercube"):
  with members:
    - "learning to hash for indexing big data a survey"
    - "high speed hashing for integers and strings"
    - "locality sensitive hashing without false negatives"
    - "probabilistic polynomials and hamming nearest neighbors"
    - "lower bounds on near neighbor search via metric expansion"
    - "optimal lower bounds for locality sensitive hashing except when q is tiny"

Connected through hyperedge HE-6 (induced by source paper "optimal hashing based time space trade offs for approximate near neighbors"):
  with members:
    - "unifying the landscape of cell probe lower bounds"

Connected through hyperedge HE-7 (induced by source paper "coveringlsh locality sensitive hashing without false negatives"):
  with members:
    - "optimal las vegas locality sensitive data structures"
    - "optimal hashing based time space trade offs for approximate near neighbors"
    - "tradeoffs for nearest neighbors on the sphere"
    - "optimal data dependent hashing for approximate near neighbors"
    - "probabilistic polynomials and hamming nearest neighbors"
    - "beyond locality sensitive hashing"
    - "optimal lower bounds for locality sensitive hashing except when q is tiny"
    - "entropy based nearest neighbor search in high dimensions"

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.DB(Databases)