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 "sublinear algorithms for gap edit distance"):
  Abstract: The edit distance is a way of quantifying how similar two strings are to one another by counting the minimum number of character insertions, deletions, and substitutions required to transform one string into the other. A simple dynamic programming computes the edit distance between two strings of length $n$ in $O(n^2)$ time, and a more sophisticated algorithm runs in time $O(n+t^2)$ when the edit distance is $t$ [Landau, Myers and Schmidt, SICOMP 1998]. In pursuit of obtaining faster running time, the last couple of decades have seen a flurry of research on approximating edit distance, including polylogarithmic approximation in near-linear time [Andoni, Krauthgamer and Onak, FOCS 2010], and a constant-factor approximation in subquadratic time [Chakrabarty, Das, Goldenberg, Koucký and Saks, FOCS 2018]. #R##N#We study sublinear-time algorithms for small edit distance, which was investigated extensively because of its numerous applications. Our main result is an algorithm for distinguishing whether the edit distance is at most $t$ or at least $t^2$ (the quadratic gap problem) in time $\tilde{O}(\frac{n}{t}+t^3)$. This time bound is sublinear roughly for all $t$ in $[\omega(1), o(n^{1/3})]$, which was not known before. The best previous algorithms solve this problem in sublinear time only for $t=\omega(n^{1/3})$ [Andoni and Onak, STOC 2009]. #R##N#Our algorithm is based on a new approach that adaptively switches between uniform sampling and reading contiguous blocks of the input strings. In contrast, all previous algorithms choose which coordinates to query non-adaptively. Moreover, it can be extended to solve the $t$ vs $t^{2-\epsilon}$ gap problem in time $\tilde{O}(\frac{n}{t^{1-\epsilon}}+t^3)$.

Includes members:
  Member "approximating lcs in linear time beating the sqrt n barrier":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "streaming with oracle new streaming algorithms for edit distance and lcs")
  Member "approximating edit distance within constant factor in truly sub quadratic time":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "streaming with oracle new streaming algorithms for edit distance and lcs")
  Member "approximating edit distance in truly subquadratic time quantum and mapreduce":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "equivalence classes and conditional hardness in massively parallel computations")
  Member "streaming algorithms for computing edit distance without exploiting suffix trees":
    also connected through other hyperedges:
      - Hyperedge HE-4 (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-5 (induced by source paper "streaming with oracle new streaming algorithms for edit distance and lcs")
  Member "linear time sequence comparison using minimal absent words applications":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "alignment free sequence comparison using absent words")
  Member "simulating branching programs with edit distance and friends or a polylog shaved is a lower bound made":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "streaming with oracle new streaming algorithms for edit distance and lcs")
  Member "quadratic conditional lower bounds for string problems and dynamic time warping":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "streaming with oracle new streaming algorithms for edit distance and lcs")

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.CC(Computational Complexity)