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 "embedjoin efficient edit similarity joins via embeddings"):
  Abstract: We study the problem of edit similarity joins, where given a set of strings and a threshold value $K$, we need to output all the pairs of strings whose edit distances are at most $K$. Edit similarity join is a fundamental operation in numerous applications such as data cleaning/integration, bioinformatics, natural language processing, and has been identified as a primitive operator for database systems. This problem has been studied extensively in the literature. However, we observed that all the existing algorithms fall short on long strings and large distance thresholds. #R##N#In this paper we propose an algorithm named \ebdjoin\ that scales very well with string length and distance threshold. Our algorithm is built on the recent advance of metric embeddings for edit distance, and is very different from all of the previous approaches. We demonstrate via an extensive set of experiments that \ebdjoin\ significantly outperforms the previous best algorithms on long strings and large distance thresholds. For example, on a collection of 20,000 real-world DNA sequences each of length 20,000 and a distance threshold that is 1\% of the string length (1\% errors), the previous best algorithms that we have tested cannot finish in 10 hours, while \ebdjoin\ finished in less than 6 minutes. Moreover, \ebdjoin\ scales very well up to 20\% errors which is critical in applications such as bioinformatics, and is far beyond the reach of existing algorithms.

Includes members:
  Member "streaming algorithms for computing edit distance without exploiting suffix trees":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "dynamic time warping in strongly subquadratic time algorithms for the low distance regime and approximate evaluation")
  Member "edit distance sketching streaming and document exchange":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "streaming with oracle new streaming algorithms for edit distance and lcs")
  Member "edit distance cannot be computed in strongly subquadratic time unless seth is false":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "streaming with oracle new streaming algorithms for edit distance and lcs")
  Member "pass join a partition based method for similarity joins":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "blocking and filtering techniques for entity resolution a survey")
  Member "bayesian locality sensitive hashing for fast similarity search":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "blocking and filtering techniques for entity resolution a survey")

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.DB(Databases)