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: "tight lower bounds for data dependent locality sensitive hashing"
  Abstract: We prove a tight lower bound for the exponent $\rho$ for data-dependent Locality-Sensitive Hashing schemes, recently used to design efficient solutions for the $c$-approximate nearest neighbor search. In particular, our lower bound matches the bound of $\rho\le \frac{1}{2c-1}+o(1)$ for the $\ell_1$ space, obtained via the recent algorithm from [Andoni-Razenshteyn, STOC'15]. #R##N#In recent years it emerged that data-dependent hashing is strictly superior to the classical Locality-Sensitive Hashing, when the hash function is data-independent. In the latter setting, the best exponent has been already known: for the $\ell_1$ space, the tight bound is $\rho=1/c$, with the upper bound from [Indyk-Motwani, STOC'98] and the matching lower bound from [O'Donnell-Wu-Zhou, ITCS'11]. #R##N#We prove that, even if the hashing is data-dependent, it must hold that $\rho\ge \frac{1}{2c-1}-o(1)$. To prove the result, we need to formalize the exact notion of data-dependent hashing that also captures the complexity of the hash functions (in addition to their collision properties). Without restricting such complexity, we would allow for obviously infeasible solutions such as the Voronoi diagram of a dataset. To preclude such solutions, we require our hash functions to be succinct. This condition is satisfied by all the known algorithmic results.

Connected through hyperedge HE-1 (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-2 (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"
    - "optimal data dependent hashing for approximate near neighbors"
    - "optimal lower bounds for locality sensitive hashing except when q is tiny"
    - "lower bounds on locality sensitive hashing"

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 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"
    - "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:
    - "optimal las vegas locality sensitive data structures"
    - "polynomial representations of threshold functions and algorithmic applications"
    - "learning to hash for indexing big data a survey"
    - "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 "local density estimation in high dimensions"):
  with members:
    - "fast cross polytope locality sensitive hashing"
    - "the role of context types and dimensionality in learning word embeddings"
    - "node2vec scalable feature learning for networks"
    - "deepwalk online learning of social representations"

Connected through hyperedge HE-6 (induced by source paper "distance sensitive hashing"):
  with members:
    - "approximate furthest neighbor with application to annulus query"
    - "embedjoin efficient edit similarity joins via embeddings"
    - "parameter free locality sensitive hashing for spherical range reporting"
    - "sub linear privacy preserving near neighbor search with untrusted server on large scale datasets"
    - "a framework for similarity search with space time tradeoffs using locality sensitive filtering"
    - "optimal hashing based time space trade offs for approximate near neighbors"
    - "on the complexity of inner product similarity join"
    - "set similarity search beyond minhash"

Connected through hyperedge HE-7 (induced by source paper "lattice based locality sensitive hashing is optimal"):
  with members:
    - "solving the closest vector problem in 2 n time the discrete gaussian strikes again"
    - "finding a closest point in a lattice of voronoi s first kind"
    - "short paths on the voronoi graph and the closest vector problem with preprocessing"
    - "optimal lower bounds for locality sensitive hashing except when q is tiny"

Connected through hyperedge HE-8 (induced by source paper "lower bounds on time space trade offs for approximate near neighbors"):
  with members:
    - "randomized approximate nearest neighbor search with limited adaptivity"
    - "simple average case lower bounds for approximate near neighbor from isoperimetric inequalities"
    - "exploiting the structure stochastic gradient methods using raw clusters"
    - "explicit correlation amplifiers for finding outlier correlations in deterministic subquadratic time"
    - "scalability and total recall with fast coveringlsh"
    - "learning to hash for indexing big data a survey"
    - "a faster subquadratic algorithm for finding outlier correlations"
    - "practical and optimal lsh for angular distance"

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)