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 "approximating cycles in directed graphs fast algorithms for girth and roundtrip spanners"):
  Abstract: The girth of a graph, i.e. the length of its shortest cycle, is a fundamental graph parameter. Unfortunately all known algorithms for computing, even approximately, the girth and girth-related structures in directed weighted $m$-edge and $n$-node graphs require $\Omega(\min\{n^{\omega}, mn\})$ time (for $2\leq\omega<2.373$). In this paper, we drastically improve these runtimes as follows: #R##N#* Multiplicative Approximations in Nearly Linear Time: We give an algorithm that in $\widetilde{O}(m)$ time computes an $\widetilde{O}(1)$-multiplicative approximation of the girth as well as an $\widetilde{O}(1)$-multiplicative roundtrip spanner with $\widetilde{O}(n)$ edges with high probability (w.h.p). #R##N#* Nearly Tight Additive Approximations: For unweighted graphs and any $\alpha \in (0,1)$ we give an algorithm that in $\widetilde{O}(mn^{1 - \alpha})$ time computes an $O(n^\alpha)$-additive approximation of the girth w.h.p, and partially derandomize it. We show that the runtime of our algorithm cannot be significantly improved without a breakthrough in combinatorial Boolean matrix multiplication. #R##N#Our main technical contribution to achieve these results is the first nearly linear time algorithm for computing roundtrip covers, a directed graph decomposition concept key to previous roundtrip spanner constructions. Previously it was not known how to compute these significantly faster than $\Omega(\min\{n^\omega, mn\})$ time. Given the traditional difficulty in efficiently processing directed graphs, we hope our techniques may find further applications.

Includes members:
  Member "routing under balance":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "faster energy maximization for faster maximum flow")
  Member "an improved combinatorial algorithm for boolean matrix multiplication":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "engineering boolean matrix multiplication for multiple accelerator shared memory architectures")
  Member "powers of tensors and fast matrix multiplication":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "new algorithms and hardness for incremental single source shortest paths in directed graphs")
  Member "a note on cut approximators and approximating undirected max flows":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "routing under balance")
  Member "parallel graph decompositions using random shifts":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "scattering and sparse partitions and their applications")
  Member "faster all pairs shortest paths via circuit complexity":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deterministic algorithms for decremental approximate shortest paths faster and simpler")
  Member "fast approximation algorithms for cut based problems in undirected graphs":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a deterministic algorithm for balanced cut with applications to dynamic connectivity flows and beyond")
  Member "electrical flows laplacian systems and faster approximation of maximum flow in undirected graphs":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a spectral approach to network design")

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)