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: "locality sensitive hashing without false negatives for l_p"
  Abstract: In this paper, we show a construction of locality-sensitive hash functions without false negatives, i.e., which ensure collision for every pair of points within a given radius R in d dimensional space equipped with \(l_p\) norm when \(p \in [1,\infty ]\). Furthermore, we show how to use these hash functions to solve the c-approximate nearest neighbor search problem without false negatives. Namely, if there is a point at distance R, we will certainly report it and points at distance greater than cR will not be reported for \(c=\varOmega (\sqrt{d},d^{1-\frac{1}{p}})\). The constructed algorithms work:#R##N##R##N##R##N#with preprocessing time \(\mathcal {O}(n \log (n))\) and sublinear expected query time,#R##N##R##N##R##N#with preprocessing time \(\mathcal {O}(\mathrm {poly}(n))\) and expected query time \(\mathcal {O}(\log (n))\).

Connected through hyperedge HE-1 (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"
    - "explicit correlation amplifiers for finding outlier correlations in deterministic subquadratic time"
    - "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-2 (induced by source paper "on fast bounded locality sensitive hashing"):
  with members:
    - "locality sensitive hashing without false negatives"

Connected through hyperedge HE-3 (induced by source paper "i o efficient similarity join"):
  with members:
    - "locality sensitive hashing without false negatives"
    - "skew strikes back new developments in the theory of join algorithms"

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)