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 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.

Connected through hyperedge HE-1 (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-2 (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"
    - "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"
    - "practical and optimal lsh for angular distance"

Connected through hyperedge HE-3 (induced by source paper "optimal las vegas approximate near neighbors in ell_p"):
  with members:
    - "approximate nearest neighbor search in high dimensions"
    - "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"
    - "optimal approximate matrix product in terms of stable rank"

Connected through hyperedge HE-4 (induced by source paper "approximate nearest neighbor search in high dimensions"):
  with members:
    - "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"
    - "locality sensitive hashing without false negatives"
    - "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-5 (induced by source paper "neural distributed autoassociative memories a survey"):
  with members:
    - "autoscaling bloom filter controlling trade off between true and false positives"
    - "dense associative memory is robust to adversarial inputs"
    - "fast similarity sketching"
    - "associative memories to accelerate approximate nearest neighbor search"
    - "dense associative memory for pattern recognition"
    - "set similarity search beyond minhash"
    - "distance sensitive bloom filters without false negatives"
    - "representing objects relations and sequences"

Connected through hyperedge HE-6 (induced by source paper "approximate nearest neighbors search without false negatives for l 2"):
  with members:
    - "on fast bounded locality sensitive hashing"
    - "locality sensitive hashing without false negatives"

Connected through hyperedge HE-7 (induced by source paper "coveringlsh locality sensitive hashing without false negatives"):
  with members:
    - "optimal hashing based time space trade offs for approximate near neighbors"
    - "scalability and total recall with fast coveringlsh"
    - "tradeoffs for nearest neighbors on the sphere"
    - "optimal data dependent hashing for approximate near neighbors"
    - "probabilistic polynomials and hamming nearest neighbors"
    - "beyond locality sensitive hashing"
    - "optimal lower bounds for locality sensitive hashing except when q is tiny"
    - "entropy based nearest neighbor search in high dimensions"

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)