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 lower bounds for locality sensitive hashing except when q is tiny"):
  Abstract: We study lower bounds for Locality Sensitive Hashing (LSH) in the strongest setting: point sets in {0,1}^d under the Hamming distance. Recall that here H is said to be an (r, cr, p, q)-sensitive hash family if all pairs x, y in {0,1}^d with dist(x,y) at most r have probability at least p of collision under a randomly chosen h in H, whereas all pairs x, y in {0,1}^d with dist(x,y) at least cr have probability at most q of collision. Typically, one considers d tending to infinity, with c fixed and q bounded away from 0. #R##N#For its applications to approximate nearest neighbor search in high dimensions, the quality of an LSH family H is governed by how small its "rho parameter" rho = ln(1/p)/ln(1/q) is as a function of the parameter c. The seminal paper of Indyk and Motwani showed that for each c, the extremely simple family H = {x -> x_i : i in d} achieves rho at most 1/c. The only known lower bound, due to Motwani, Naor, and Panigrahy, is that rho must be at least .46/c (minus o_d(1)). #R##N#In this paper we show an optimal lower bound: rho must be at least 1/c (minus o_d(1)). This lower bound for Hamming space yields a lower bound of 1/c^2 for Euclidean space (or the unit sphere) and 1/c for the Jaccard distance on sets; both of these match known upper bounds. Our proof is simple; the essence is that the noise stability of a boolean function at e^{-t} is a log-convex function of t.

Includes members:
  Member "entropy based nearest neighbor search in high dimensions":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "algorithms for similarity search and pseudorandomness")
  Member "lower bounds on locality sensitive hashing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "algorithms for similarity search and pseudorandomness")

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)