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 "approximate similarity search under edit distance using locality sensitive hashing"):
  Abstract: Edit distance similarity search, also called approximate pattern matching, is a fundamental problem with widespread applications. The goal of the problem is to preprocess n strings of length d to quickly answer queries q of the form: if there is a database string within edit distance r of q, return a database string within edit distance cr of q. A data structure solving this problem is analyzed using two criteria: the amount of extra space used in preprocessing, and the expected time to answer a query. #R##N#Previous approaches to this problem have either used trie-based methods, which give exact solutions at the cost of expensive queries, or embeddings, which only work for large (superconstant) values of c. #R##N#In this work we achieve the first bounds for any approximation factor c, via a simple and easy-to-implement hash function. This gives a running time of $\tilde{O}(d3^rn^{1/c})$, with space $\tilde{O}(3^r n^{1 + 1/c} + dn)$. We show how to apply these ideas to the closely-related Approximate Nearest Neighbor problem for edit distance, obtaining similar time bounds.

Includes members:
  Member "constant factor approximation of near linear edit distance in near linear time":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "streaming with oracle new streaming algorithms for edit distance and lcs")
  Member "race sub linear memory sketches for approximate near neighbor search on streaming data":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sub linear race sketches for approximate kernel density estimation on streaming data")
  Member "near linear time insertion deletion codes and 1 e approximating edit distance via indexing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "optimally resilient codes for list decoding from insertions and deletions")
  Member "constant factor approximations to edit distance on far input pairs in nearly linear time":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "streaming with oracle new streaming algorithms for edit distance and lcs")
  Member "approximate nearest neighbor search in high dimensions":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "copt coordinated optimal transport on graphs")
  Member "set similarity search for skewed data":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "lsf join locality sensitive filtering for distributed all pairs set similarity under skew")
  Member "hardness of approximate nearest neighbor search":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "on the i o complexity of the k nearest neighbor problem")
  Member "fast entropy bounded string dictionary look up with mismatches":
    also connected through other hyperedges:
      (none)

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)