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: "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.

Connected through hyperedge HE-1 (induced by source paper "a survey on deep hashing methods"):
  with members:
    - "end to end supervised product quantization for image search and retrieval"
    - "distillhash unsupervised deep hashing by distilling data pairs"
    - "deep triplet quantization"
    - "hadamard codebook based deep hashing"
    - "central similarity hashing via hadamard matrix"
    - "deep attention guided hashing"
    - "deep spherical quantization for image search"
    - "deep ordinal hashing with spatial attention"

Connected through hyperedge HE-2 (induced by source paper "algorithms for similarity search and pseudorandomness"):
  with members:
    - "hardness of approximate nearest neighbor search"
    - "fast locality sensitive hashing for approximate near neighbor search"
    - "fast similarity sketching"
    - "embedjoin efficient edit similarity joins via embeddings"
    - "optimal densification for fast and accurate minwise hashing"
    - "parameter free locality sensitive hashing for spherical range reporting"
    - "scalable and robust set similarity join"
    - "fast cross polytope locality sensitive hashing"

Connected through hyperedge HE-3 (induced by source paper "subsets and supermajorities unifying hashing based set similarity search"):
  with members:
    - "norm ranging lsh for maximum inner product search"
    - "an equivalence class for orthogonal vectors"
    - "hardness of approximate nearest neighbor search"
    - "fast similarity sketching"
    - "distributed pcp theorems for hardness of approximation in p"
    - "optimal hashing based time space trade offs for approximate near neighbors"
    - "on the complexity of inner product similarity join"
    - "polynomial representations of threshold functions and algorithmic applications"

Connected through hyperedge HE-4 (induced by source paper "similarity problems in high dimensions"):
  with members:
    - "approximate furthest neighbor with application to annulus query"
    - "distributed pcp theorems for hardness of approximation in p"
    - "embedjoin efficient edit similarity joins via embeddings"
    - "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"
    - "set similarity search beyond minhash"
    - "distance sensitive bloom filters without false negatives"

Connected through hyperedge HE-5 (induced by source paper "graph based nearest neighbor search from practice to theory"):
  with members:
    - "graph based nearest neighbor search promises and failures"
    - "optimization of indexing based on k nearest neighbor graph for proximity search in high dimensional data"
    - "ann benchmarks a benchmarking tool for approximate nearest neighbor algorithms"
    - "graph based time space trade offs for approximate near neighbors"
    - "fast approximate nearest neighbor search with navigating spreading out graphs"
    - "tight lower bounds for data dependent locality sensitive hashing"
    - "optimal data dependent hashing for approximate near neighbors"
    - "lower bounds on locality sensitive hashing"

Connected through hyperedge HE-6 (induced by source paper "hardness of approximate nearest neighbor search"):
  with members:
    - "on the hardness of approximate and exact bichromatic maximum inner product"
    - "distributed pcp theorems for hardness of approximation in p"
    - "optimal las vegas locality sensitive data structures"
    - "randomized approximate nearest neighbor search with limited adaptivity"
    - "the curse of medium dimension for geometric problems in almost every norm"
    - "optimal hashing based time space trade offs for approximate near neighbors"
    - "polynomial representations of threshold functions and algorithmic applications"
    - "a faster subquadratic algorithm for finding outlier correlations"

Connected through hyperedge HE-7 (induced by source paper "a new algorithm for finding closest pair of vectors"):
  with members:
    - "an illuminating algorithm for the light bulb problem"
    - "fast feasible and unfeasible matrix multiplication"
    - "distributed pcp theorems for hardness of approximation in p"
    - "generating families of practical fast matrix multiplication algorithms"
    - "polynomial representations of threshold functions and algorithmic applications"
    - "explicit correlation amplifiers for finding outlier correlations in deterministic subquadratic time"
    - "a faster subquadratic algorithm for finding outlier correlations"
    - "probabilistic polynomials and hamming nearest neighbors"

Connected through hyperedge HE-8 (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"
    - "scalability and total recall with fast coveringlsh"
    - "tight lower bounds for data dependent locality sensitive hashing"
    - "practical and optimal lsh for angular distance"
    - "locality sensitive hashing without false negatives"

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)