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"
  Abstract: We consider a new construction of locality-sensitive hash functions for Hamming space that is covering in the sense that is it guaranteed to produce a collision for every pair of vectors within a given radius $r$. The construction is efficient in the sense that the expected number of hash collisions between vectors at distance $cr$, for a given $c>1$, comes close to that of the best possible data independent LSH without the covering guarantee, namely, the seminal LSH construction of Indyk and Motwani (FOCS '98). The efficiency of the new construction essentially matches their bound if $cr = \log(n)/k$, where $n$ is the number of points in the data set and $k\in {\bf N}$, and differs from it by at most a factor $\ln(4)<1.4$ in the exponent for general values of $cr$. As a consequence, LSH-based similarity search in Hamming space can avoid the problem of false negatives at little or no cost in efficiency.

Connected through hyperedge HE-1 (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-2 (induced by source paper "nearest neighbor decoding for tardos fingerprinting codes"):
  with members:
    - "ann benchmarks a benchmarking tool for approximate nearest neighbor algorithms"
    - "optimal hashing based time space trade offs for approximate near neighbors"
    - "optimal sequential fingerprinting wald vs tardos"
    - "practical and optimal lsh for angular distance"
    - "optimal symmetric tardos traitor tracing schemes"
    - "capacities and capacity achieving decoders for various fingerprinting games"
    - "enhanced blind decoding of tardos codes with new map based functions"
    - "discrete distributions in the tardos scheme revisited"

Connected through hyperedge HE-3 (induced by source paper "race sub linear memory sketches for approximate near neighbor search on streaming data"):
  with members:
    - "approximate nearest neighbors in limited space"
    - "hashing based estimators for kernel density in high dimensions"
    - "billion scale similarity search with gpus"
    - "when hashes met wedges a distributed algorithm for finding high similarity vectors"
    - "near optimal euclidean metric compression"
    - "practical and optimal lsh for angular distance"
    - "beyond locality sensitive hashing"
    - "new constructions of rip matrices with fast multiplication and fewer rows"

Connected through hyperedge HE-4 (induced by source paper "covering codes for insertions and deletions"):
  with members:
    - "an overview of capacity results for synchronization channels"
    - "near linear time insertion deletion codes and 1 e approximating edit distance via indexing"
    - "optimally resilient codes for list decoding from insertions and deletions"
    - "polynomial time decodable codes for the binary deletion channel"
    - "an improved bound on the fraction of correctable deletions"
    - "coding for interactive communication correcting insertions and deletions"
    - "generalized sphere packing bound"
    - "a characterization of the number of subsequences obtained via the deletion channel"

Connected through hyperedge HE-5 (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"
    - "optimal approximate matrix product in terms of stable rank"

Connected through hyperedge HE-6 (induced by source paper "approximate nearest neighbor search in high dimensions"):
  with members:
    - "optimal las vegas locality sensitive data structures"
    - "polynomial representations of threshold functions and algorithmic applications"
    - "learning to hash for indexing big data a survey"
    - "tight lower bounds for data dependent locality sensitive hashing"
    - "approximate nearest neighbor search for ell_p spaces 2 p infty via embeddings"
    - "hashing for similarity search a survey"
    - "sparser johnson lindenstrauss transforms"
    - "optimal lower bounds for locality sensitive hashing except when q is tiny"

Connected through hyperedge HE-7 (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"
    - "locality sensitive hashing without false negatives for l_p"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "scalable and robust set similarity join"):
  with members:
    - "embedjoin efficient edit similarity joins via embeddings"
    - "parameter free locality sensitive hashing for spherical range reporting"
    - "optimal hashing based time space trade offs for approximate near neighbors"
    - "set similarity search beyond minhash"

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)