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 "optimal las vegas locality sensitive data structures"):
  Abstract: We show that approximate similarity (near neighbour) search can be solved in high dimensions with performance matching state of the art (data independent) Locality Sensitive Hashing, but with a guarantee of no false negatives. #R##N#Specifically, we give two data structures for common problems. #R##N#For $c$-approximate near neighbour in Hamming space we get query time $dn^{1/c+o(1)}$ and space $dn^{1+1/c+o(1)}$ matching that of \cite{indyk1998approximate} and answering a long standing open question from~\cite{indyk2000dimensionality} and~\cite{pagh2016locality} in the affirmative. #R##N#By means of a new deterministic reduction from $\ell_1$ to Hamming we also solve $\ell_1$ and $\ell_2$ with query time $d^2n^{1/c+o(1)}$ and space $d^2 n^{1+1/c+o(1)}$. #R##N#For $(s_1,s_2)$-approximate Jaccard similarity we get query time $dn^{\rho+o(1)}$ and space $dn^{1+\rho+o(1)}$, $\rho=\log\frac{1+s_1}{2s_1}\big/\log\frac{1+s_2}{2s_2}$, when sets have equal size, matching the performance of~\cite{tobias2016}. #R##N#The algorithms are based on space partitions, as with classic LSH, but we construct these using a combination of brute force, tensoring, perfect hashing and splitter functions a la~\cite{naor1995splitters}. We also show a new dimensionality reduction lemma with 1-sided error.

Includes members:
  Member "parameter free locality sensitive hashing for spherical range reporting":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "approximating text to pattern hamming distances")
  Member "a framework for similarity search with space time tradeoffs using locality sensitive filtering":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "lsf join locality sensitive filtering for distributed all pairs set similarity under skew")
  Member "optimal hashing based time space trade offs for approximate near neighbors":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on the i o complexity of the k nearest neighbor problem")
  Member "locality sensitive hashing without false negatives for l_p":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on fast bounded locality sensitive hashing")
  Member "explicit correlation amplifiers for finding outlier correlations in deterministic subquadratic time":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a faster algorithm enumerating relevant features over finite fields")
  Member "distance sensitive bloom filters without false negatives":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "similarity problems in high dimensions")
  Member "scalability and total recall with fast coveringlsh":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "optimal las vegas approximate near neighbors in ell_p")
  Member "practical and optimal lsh for angular distance":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "differentiable reasoning over a virtual knowledge base")

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)