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 "on the complexity of bwt runs minimization via alphabet reordering"):
  Abstract: We present the first set of results on the computational complexity of minimizing BWT-runs via alphabet reordering. We prove that the decision version of this problem is NP-complete and cannot be solved in time $2^{o(\sigma)}n$ unless the Exponential Time Hypothesis fails, where $\sigma$ is the size of the alphabet. Moreover, we show that optimization variations of this problem yield strong inapproximability results. In doing so we relate two previously disparate topics: the size of a path cover of a graph and the number of runs in the BWT of a text. This provides a surprising connection between problems on graphs and string compression. As a result we are able to prove (all assuming P $\neq$ NP): (i) No PTAS exists if we define the cost of a solution as exactly the number of runs exceeding $\sigma$; (ii) For all $\delta > 0$, no polytime $\epsilon n^{1/2}$-approximation algorithm exists for $\epsilon > 0$ small enough if we consider the number of runs exceeding $(1+\delta)\sigma$ as the cost of a solution. In this case the problem is APX-hard as well. To the best of our knowledge these are the first ever inapproximability results pertaining to the BWT. In addition, by relating recent results in the field of dictionary compression, we demonstrate that if we define cost purely as the number of runs, we obtain a $\log^2 n$-approximation algorithm. Finally, we provide an efficient algorithm for the more restricted problem of finding an optimal ordering on a subset of symbols (occurring only once) under ordering constraints which runs in optimal time for small values of $\sigma$. We also look at a version of the problem on the newly discovered class of graphs with BWT like properties called Wheeler graphs. Here also we show NP-hardness results on a related problem which we call Source Ordering.

Includes members:
  Member "on the hardness and inapproximability of recognizing wheeler graphs":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "graphs cannot be indexed in polynomial time for sub quadratic time string matching unless seth fails")
  Member "resolution of the burrows wheeler transform conjecture":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on extensions of maximal repeats in compressed strings")
  Member "string synchronizing sets sublinear time bwt construction and optimal lce data structure":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "approximating text to pattern hamming distances")
  Member "tunneling on wheeler graphs":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on the hardness and inapproximability of recognizing wheeler graphs")
  Member "efficient construction of a complete index for pan genomics read alignment":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "matching reads to many genomes with the r index")
  Member "optimal construction of compressed indexes for highly repetitive texts":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "resolution of the burrows wheeler transform conjecture")
  Member "at the roots of dictionary compression string attractors":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "towards a definitive measure of repetitiveness")
  Member "optimal time text indexing in bwt runs bounded space":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "fast indexes for gapped pattern matching")

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)