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 "streaming complexity of spanning tree computation"):
  Abstract: The semi-streaming model is a variant of the streaming model frequently used for the computation of graph problems. It allows the edges of an $n$-node input graph to be read sequentially in $p$ passes using $\tilde{O}(n)$ space. In this model, some graph problems, such as spanning trees and $k$-connectivity, can be exactly solved in a single pass; while other graph problems, such as triangle detection and unweighted all-pairs shortest paths, are known to require $\tilde{\Omega}(n)$ passes to compute. For many fundamental graph problems, the tractability in these models is open. In this paper, we study the tractability of computing some standard spanning trees. Our results are: #R##N#(1) Maximum-Leaf Spanning Trees. This problem is known to be APX-complete with inapproximability constant $\rho\in[245/244,2)$. By constructing an $\varepsilon$-MLST sparsifier, we show that for every constant $\varepsilon > 0$, MLST can be approximated in a single pass to within a factor of $1+\varepsilon$ w.h.p. (albeit in super-polynomial time for $\varepsilon \le \rho-1$ assuming $\mathrm{P} \ne \mathrm{NP}$). #R##N#(2) BFS Trees. It is known that BFS trees require $\omega(1)$ passes to compute, but the naive approach needs $O(n)$ passes. We devise a new randomized algorithm that reduces the pass complexity to $O(\sqrt{n})$, and it offers a smooth tradeoff between pass complexity and space usage. #R##N#(3) DFS Trees. The current best algorithm by Khan and Mehta {[}STACS 2019{]} takes $\tilde{O}(h)$ passes, where $h$ is the height of computed DFS trees. Our contribution is twofold. First, we provide a simple alternative proof of this result, via a new connection to sparse certificates for $k$-node-connectivity. Second, we present a randomized algorithm that reduces the pass complexity to $O(\sqrt{n})$, and it also offers a smooth tradeoff between pass complexity and space usage.

Includes members:
  Member "characterising at free graphs with bfs":
    also connected through other hyperedges:
      (none)
  Member "a simple test on 2 vertex and 2 edge connectivity":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a simple certifying algorithm for 3 edge connectivity")

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)