human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "set similarity search beyond minhash"):
  Abstract: We consider the problem of approximate set similarity search under Braun-Blanquet similarity $B(\mathbf{x}, \mathbf{y}) = |\mathbf{x} \cap \mathbf{y}| / \max(|\mathbf{x}|, |\mathbf{y}|)$. The $(b_2, b_2)$-approximate Braun-Blanquet similarity search problem is to preprocess a collection of sets $P$ such that, given a query set $\mathbf{q}$, if there exists $\mathbf{x} \in P$ with $B(\mathbf{q}, \mathbf{x}) \geq b_1$, then we can efficiently return $\mathbf{x}' \in P$ with $B(\mathbf{q}, \mathbf{x}') > b_2$. #R##N#We present a simple data structure that solves this problem with space usage $O(n^{1+\rho}\log n + \sum_{\mathbf{x} \in P}|\mathbf{x}|)$ and query time $O(|\mathbf{q}|n^{\rho} \log n)$ where $n = |P|$ and $\rho = \log(1/b_1)/\log(1/b_2)$. Making use of existing lower bounds for locality-sensitive hashing by O'Donnell et al. (TOCT 2014) we show that this value of $\rho$ is tight across the parameter space, i.e., for every choice of constants $0 < b_2 < b_1 < 1$. #R##N#In the case where all sets have the same size our solution strictly improves upon the value of $\rho$ that can be obtained through the use of state-of-the-art data-independent techniques in the Indyk-Motwani locality-sensitive hashing framework (STOC 1998) such as Broder's MinHash (CCS 1997) for Jaccard similarity and Andoni et al.'s cross-polytope LSH (NIPS 2015) for cosine similarity. Surprisingly, even though our solution is data-independent, for a large part of the parameter space we outperform the currently best data-dependent method by Andoni and Razenshteyn (STOC 2015).

Includes members:
  Member "a framework for similarity search with space time tradeoffs using locality sensitive filtering":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "lsf join locality sensitive filtering for distributed all pairs set similarity under skew")
  Member "optimal hashing based time space trade offs for approximate near neighbors":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on the i o complexity of the k nearest neighbor problem")
  Member "on the complexity of inner product similarity join":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "lsf join locality sensitive filtering for distributed all pairs set similarity under skew")
  Member "tight lower bounds for data dependent locality sensitive hashing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "algorithms for similarity search and pseudorandomness")
  Member "practical and optimal lsh for angular distance":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "differentiable reasoning over a virtual knowledge base")
  Member "tradeoffs for nearest neighbors on the sphere":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "algorithms for similarity search and pseudorandomness")
  Member "optimal data dependent hashing for approximate near neighbors":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "autoblock a hands off blocking framework for entity matching")
  Member "beyond locality sensitive hashing":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "dynamic streaming spectral sparsification in nearly linear time and space")

Question: Please tell me which class the source paper 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)