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 "fine grained complexity of analyzing compressed data quantifying improvements over decompress and solve"):
  Abstract: Can we analyze data without decompressing it? As our data keeps growing, understanding the time complexity of problems on compressed inputs, rather than in convenient uncompressed forms, becomes more and more relevant. Suppose we are given a compression of size n of data that originally has size N, and we want to solve a problem with time complexity T(&#x22C5;). The na&#x00EF;ve strategy of decompress-and-solve gives time T(N), whereas the gold standard is time T(n): to analyze the compression as efficiently as if the original data was small.We restrict our attention to data in the form of a string (text, files, genomes, etc.) and study the most ubiquitous tasks. While the challenge might seem to depend heavily on the specific compression scheme, most methods of practical relevance (Lempel-Ziv-family, dictionary methods, and others) can be unified under the elegant notion of Grammar-Compressions. A vast literature, across many disciplines, established this as an influential notion for Algorithm design.We introduce a direly needed framework for proving (conditional) lower bounds in this field, allowing us to assess whether decompress-and-solve can be improved, and by how much. Our main results are:&#x2022; The O(nN&#x221A;log(N/n)) bound for LCS and the O(min(N log N, nM)) bound for Pattern Matching with Wildcards are optimal up to N^{o(1)} factors, under the Strong Exponential Time Hypothesis. (Here, M denotes the uncompressed length of the compressed pattern.)&#x2022; Decompress-and-solve is essentially optimal for Context-Free Grammar Parsing and RNA Folding, under the k-Clique conjecture.&#x2022; We give an algorithm showing that decompress-and-solve is not optimal for Disjointness.

Includes members:
  Member "truly sub cubic algorithms for language edit distance and rna folding via fast bounded difference min plus product":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "streaming with oracle new streaming algorithms for edit distance and lcs")
  Member "why is it hard to beat o n 2 for longest common weakly increasing subsequence":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a sub quadratic algorithm for the longest common increasing subsequence problem")
  Member "a dichotomy for regular expression membership testing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "more consequences of falsifying seth and the orthogonal vectors conjecture")
  Member "conditional lower bound for rna folding problem":
    also connected through other hyperedges:
      (none)
  Member "the k mismatch problem revisited":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "approximating text to pattern hamming distances")
  Member "clustered integer 3sum via additive combinatorics":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "all non trivial variants of 3 ldt are equivalent")
  Member "which regular expression patterns are hard to match":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "graphs cannot be indexed in polynomial time for sub quadratic time string matching unless seth fails")
  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")

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)