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: "parameter free locality sensitive hashing for spherical range reporting"
  Abstract: We present a data structure for spherical range reporting on a point set S, i.e., reporting all points in S that lie within radius r of a given query point q (with a small probability of error). Our solution builds upon the Locality-Sensitive Hashing (LSH) framework of Indyk and Motwani, which represents the asymptotically best solutions to near neighbor problems in high dimensions. While traditional LSH data structures have several parameters whose optimal values depend on the distance distribution from q to the points of S (and in particular on the number of points to report), our data structure is essentially parameter-free and only takes as parameter the space the user is willing to allocate. Nevertheless, its expected query time basically matches that of an LSH data structure whose parameters have been optimally chosen for the data and query in question under the given space constraints. In particular, our data structure provides a smooth trade-off between hard queries (typically addressed by standard LSH parameter settings) and easy queries such as those where the number of points to report is a constant fraction of S, or where almost all points in S are far away from the query point. In contrast, known data structures fix LSH parameters based on certain parameters of the input alone.   The algorithm has expected query time bounded by O(t(n/t)ρ), where t is the number of points to report and ρ ∈ (0, 1) depends on the data distribution and the strength of the LSH family used. The previously best running time in high dimensions was Ω(tnρ), achieved by traditional LSH-based data structures where parameters are tuned for outputting a single point within distance r. Further, for many data distributions where the intrinsic dimensionality of the point set close to q is low, we can give improved upper bounds on the expected query time. We finally present a parameter-free way of using multi-probing, for LSH families that support it, and show that for many such families this approach allows us to get expected query time close to O(nρ + t), which is the best we can hope to achieve using LSH.

Connected through hyperedge HE-1 (induced by source paper "approximating text to pattern hamming distances"):
  with members:
    - "l p pattern matching in a stream"
    - "string synchronizing sets sublinear time bwt construction and optimal lce data structure"
    - "the streaming k mismatch problem"
    - "improved rectangular matrix multiplication using powers of the coppersmith winograd tensor"
    - "optimal hashing based time space trade offs for approximate near neighbors"
    - "polynomial representations of threshold functions and algorithmic applications"
    - "approximate hamming distance in a stream"
    - "the k mismatch problem revisited"

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"
    - "scalable and robust set similarity join"
    - "fast cross polytope locality sensitive hashing"
    - "a framework for similarity search with space time tradeoffs using locality sensitive filtering"

Connected through hyperedge HE-3 (induced by source paper "approximate similarity search under edit distance using locality sensitive hashing"):
  with members:
    - "constant factor approximation of near linear edit distance in near linear time"
    - "race sub linear memory sketches for approximate near neighbor search on streaming data"
    - "near linear time insertion deletion codes and 1 e approximating edit distance via indexing"
    - "constant factor approximations to edit distance on far input pairs in nearly linear time"
    - "approximate nearest neighbor search in high dimensions"
    - "set similarity search for skewed data"
    - "hardness of approximate nearest neighbor search"
    - "fast entropy bounded string dictionary look up with mismatches"

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"
    - "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"
    - "fast and powerful hashing using tabulation"

Connected through hyperedge HE-5 (induced by source paper "fair near neighbor search independent range sampling in high dimensions"):
  with members:
    - "independent range sampling revisited again"
    - "approximate nearest neighbor search in high dimensions"
    - "hashing based estimators for kernel density in high dimensions"
    - "sub linear privacy preserving near neighbor search with untrusted server on large scale datasets"
    - "equality of opportunity in supervised learning"
    - "sub linear privacy preserving search with untrusted server and semi honest parties"
    - "optimal hashing based time space trade offs for approximate near neighbors"
    - "probabilistic polynomials and hamming nearest neighbors"

Connected through hyperedge HE-6 (induced by source paper "fresh fr echet similarity with hashing"):
  with members:
    - "approximate nearest neighbor search in high dimensions"
    - "a fast implementation of near neighbors queries for frechet distance gis cup"
    - "locality sensitive hashing of curves"
    - "on the complexity of range searching among curves"
    - "products of euclidean metrics and applications to proximity questions among curves"
    - "fast locality sensitive hashing for approximate near neighbor search"
    - "fast locality sensitive hashing frameworks for approximate near neighbor search"
    - "spatio temporal analysis of team sports"

Connected through hyperedge HE-7 (induced by source paper "ann benchmarks a benchmarking tool for approximate nearest neighbor algorithms"):
  with members:
    - "flash randomized algorithms accelerated over cpu gpu for ultra high dimensional similarity search"
    - "billion scale similarity search with gpus"
    - "graph based time space trade offs for approximate near neighbors"
    - "scalable and robust set similarity join"
    - "hybrid lsh faster near neighbors reporting in high dimensional space"
    - "efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs"
    - "off the beaten path let s replace term based retrieval with k nn search"
    - "practical and optimal lsh for angular distance"

Connected through hyperedge HE-8 (induced by source paper "adaptive mapreduce similarity joins"):
  with members:
    - "locality sensitive hashing of curves"
    - "scalable and robust set similarity join"
    - "experimental evaluation of multi round matrix multiplication on mapreduce"
    - "i o efficient similarity join"
    - "communication steps for parallel query processing"
    - "space round tradeoffs for mapreduce computations"

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)