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 "lsf join locality sensitive filtering for distributed all pairs set similarity under skew"):
  Abstract: All-pairs set similarity is a widely used data mining task, even for large and high-dimensional datasets. Traditionally, similarity search has focused on discovering very similar pairs, for which a variety of efficient algorithms are known. However, recent work highlights the importance of finding pairs of sets with relatively small intersection sizes. For example, in a recommender system, two users may be alike even though their interests only overlap on a small percentage of items. In such systems, some dimensions are often highly skewed because they are very popular. Together these two properties render previous approaches infeasible for large input sizes. To address this problem, we present a new distributed algorithm, LSF-Join, for approximate all-pairs set similarity. The core of our algorithm is a randomized selection procedure based on Locality Sensitive Filtering. Our method deviates from prior approximate algorithms, which are based on Locality Sensitive Hashing. Theoretically, we show that LSF-Join efficiently finds most close pairs, even for small similarity thresholds and for skewed input sets. We prove guarantees on the communication, work, and maximum load of LSF-Join, and we also experimentally demonstrate its accuracy on multiple graphs.

Includes members:
  Member "hardness of bichromatic closest pair with jaccard similarity":
    also connected through other hyperedges:
      (none)
  Member "set similarity search for skewed data":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "approximate similarity search under edit distance using locality sensitive hashing")
  Member "adaptive mapreduce similarity joins":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the space complexity of inner product filters")
  Member "when hashes met wedges a distributed algorithm for finding high similarity vectors":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "race sub linear memory sketches for approximate near neighbor search on streaming data")
  Member "scalable and robust set similarity join":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "blocking and filtering techniques for entity resolution a survey")
  Member "a framework for similarity search with space time tradeoffs using locality sensitive filtering":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "algorithms for similarity search and pseudorandomness")
  Member "on the complexity of inner product similarity join":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "algorithms for similarity search and pseudorandomness")
  Member "lsh ensemble internet scale domain search":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "iclab a global longitudinal internet censorship measurement platform")

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)