human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "nearly work efficient parallel algorithm for digraph reachability"
  Abstract: One of the simplest problems on directed graphs is that of identifying the set of vertices reachable from a designated source vertex. This problem can be solved easily sequentially by performing a graph search, but efficient parallel algorithms have eluded researchers for decades. For sparse high-diameter graphs in particular, there is no known work-efficient parallel algorithm with nontrivial parallelism. This amounts to one of the most fundamental open questions in parallel graph algorithms: Is there a parallel algorithm for digraph reachability with nearly linear work? This paper shows that the answer is yes. #R##N#This paper presents a randomized parallel algorithm for digraph reachability and related problems with expected work $\tilde{O}(m)$ and span $\tilde{O}(n^{2/3})$, and hence parallelism $\tilde{\Omega}(n^{1/3})$, on any graph with $n$ vertices and $m$ arcs. This is the first parallel algorithm having both nearly linear work and strongly sublinear span. The algorithm can be extended to produce a directed spanning tree, determine whether the graph is acyclic, topologically sort the strongly connected components of the graph, or produce a directed ear decomposition of a strongly connected graph, all with similar work and span. #R##N#The main technical contribution is an \emph{efficient} Monte Carlo algorithm that, through the addition of $\tilde{O}(n)$ shortcuts, reduces the diameter of the graph to $\tilde{O}(n^{2/3})$ with high probability. While both sequential and parallel algorithms are known with those combinatorial properties, even the sequential algorithms are not efficient. This paper presents a surprisingly simple sequential algorithm that achieves the stated diameter reduction and runs in $\tilde{O}(m)$ time. Parallelizing that algorithm yields the main result, but doing so involves overcoming several other challenges.

Connected through hyperedge HE-1 (induced by source paper "faster parallel algorithm for approximate shortest path"):
  with members:
    - "preconditioning for the geometric transportation problem"
    - "parallel approximate undirected shortest paths via low hop emulators"
    - "near optimal approximate shortest paths and transshipment in distributed and streaming models"
    - "efficient algorithms for constructing very sparse spanners and emulators"
    - "approximate undirected maximum flows in o m polylog n time"
    - "a combinatorial tilde o m 3 2 time algorithm for the min cost flow problem"
    - "an almost linear time algorithm for approximate max flow in undirected graphs and its multicommodity generalizations"
    - "an efficient parallel solver for sdd linear systems"

Connected through hyperedge HE-2 (induced by source paper "improved girth approximation and roundtrip spanners"):
  with members:
    - "finding even cycles faster via capped k walks"
    - "approximating cycles in directed graphs fast algorithms for girth and roundtrip spanners"
    - "almost linear time algorithms for markov chains and new spectral primitives for directed graphs"
    - "parallel graph decompositions using random shifts"

Connected through hyperedge HE-3 (induced by source paper "efficient construction of directed hopsets and parallel approximate shortest paths"):
  with members:
    - "parallel shortest paths using radius stepping"
    - "hopsets with constant hopbound and applications to approximate shortest paths"
    - "improved parallel algorithms for spanners and hopsets"

Connected through hyperedge HE-4 (induced by source paper "theoretically efficient parallel graph algorithms can be fast and scalable"):
  with members:
    - "single source bottleneck path algorithm faster than sorting for sparse graphs"
    - "local algorithms for hierarchical dense subgraph discovery"
    - "parallelism in randomized incremental algorithms"
    - "bigsparse high performance external graph analytics"
    - "implicit decomposition for write efficient connectivity algorithms"
    - "a new efficient construction on probabilistic tree embeddings"
    - "compressing graphs and indexes with recursive graph bisection"
    - "the gap benchmark suite"

Connected through hyperedge HE-5 (induced by source paper "lower bounds on sparse spanners emulators and diameter reducing shortcuts"):
  with members:
    - "thorup zwick emulators are universally optimal hopsets"
    - "a hierarchy of lower bounds for sublinear additive spanners"
    - "linear size distance preservers"
    - "very sparse additive spanners and emulators"
    - "better distance preservers and additive spanners"
    - "the 4 3 additive spanner exponent is tight"
    - "additive spanners a simple construction"

Question: Please tell me which class the center node 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)