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 "computing and testing small connectivity in near linear time and queries via fast local cut algorithms"):
  Abstract: Consider the following "local" cut-detection problem in a directed graph: We are given a seed vertex $x$ and need to remove at most $k$ edges so that at most $\nu$ edges can be reached from $x$ (a "local" cut) or output $\bot$ to indicate that no such cut exists. If we are given query access to the input graph, then this problem can in principle be solved without reading the whole graph and with query complexity depending on $k$ and $\nu$. In this paper we consider a slack variant of this problem where, when such a cut exists, we can output a cut with up to $O(k\nu)$ edges reachable from $x$. #R##N#We present a simple randomized algorithm spending $O(k^2\nu)$ time and $O(k\nu)$ queries for the above variant, improving in particular a previous time bound of $O(k^{O(k)}\nu)$ by Chechik et al. [SODA '17]. We also extend our algorithm to handle an approximate variant. We demonstrate that these local algorithms are versatile primitives for designing substantially improved algorithms for classic graph problems by providing the following three applications. (Throughout, $\tilde O(T)$ hides $\operatorname{polylog}(T)$.) (1) A randomized algorithm for the classic $k$-vertex connectivity problem that takes near-linear time when $k=O(\operatorname{polylog}(n))$, namely $\tilde O(m+nk^3)$ time in undirected graphs. For directed graphs our $\tilde O(mk^2)$-time algorithm is near-linear when $k=O(\operatorname{polylog}(n))$. Our techniques also yield an improved approximation scheme. (2) Property testing algorithms for $k$-edge and -vertex connectivity with query complexities that are near-linear in $k$, exponentially improving the state-of-the-art. This resolves two open problems, one by Goldreich and Ron [STOC '97] and one by Orenstein and Ron [Theor. Comput Sci. '11]. (3) A faster algorithm for computing the maximal $k$-edge connected subgraphs, improving prior work of Chechik et al. [SODA '17].

Includes members:
  Member "a faster local algorithm for detecting bounded size cuts with applications to higher connectivity problems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deterministic graph cuts in subquadratic time sparse balanced and k vertex")
  Member "computing and testing small vertex connectivity in near linear time and queries":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deterministic graph cuts in subquadratic time sparse balanced and k vertex")
  Member "breaking quadratic time for small vertex connectivity and an approximation scheme":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a deterministic algorithm for balanced cut with applications to dynamic connectivity flows and beyond")
  Member "a new algorithm for decremental single source shortest paths with applications to vertex capacitated flow and cut problems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a deterministic algorithm for balanced cut with applications to dynamic connectivity flows and beyond")
  Member "expander decomposition and pruning faster stronger and simpler":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "coarse grained complexity for dynamic algorithms")
  Member "faster algorithms for computing maximal 2 connected subgraphs in sparse directed graphs":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deterministic graph cuts in subquadratic time sparse balanced and k vertex")
  Member "on sampling edges almost uniformly":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "nearly optimal edge estimation with independent set queries")
  Member "local flow partitioning for faster edge connectivity":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "improved algorithm for min cuts in distributed networks")

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)