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: "temporal cliques admit sparse spanners"
  Abstract: Let ${\cal G}=(G,\lambda)$ be a labeled graph on $n$ vertices with $\lambda:E_G\to \mathbb{N}$ a locally injective mapping that assigns to every edge a single integer label. The label is seen as a discrete time when the edge is present. This graph is {\em temporally connected} if a path exists with increasing labels from every vertex to every other vertex. In a seminal paper, Kempe, Kleinberg, and Kumar (JCSS 2002) asked whether, given such a labeled graph, a {\em sparse} subset of edges can always be found that preserves temporal connectivity if the other edges are removed -- we call such subsets {\em temporal spanners}. Recently, Axiotis and Fotakis (ICALP 2016) answered negatively, exhibiting a family of minimally connected temporal graphs with $\Omega(n^2)$ edges. The natural question then becomes whether sparse spanners can be found in specific classes of dense graphs. #R##N#In this article, we settle the question {\em positively} for complete graphs, showing that one can always remove all but $o(n^2)$ edges, whatever the labels, while preserving temporal connectivity. The best approach so far led to removing only $O(n)$ edges, leaving the asymptotic density of the graph unchanged (Akrida et al., ToCS 2017). We start by observing that the same argument can be generalized to removing $O(n^2)$ edges (a sixth of the edges). Then, using a completely different approach, we establish a gradual set of results, showing that a quarter of the edges can be removed, then half of the edges, and eventually {\em all} but $O(n \log n)$ edges. This result is robust in the sense that it extends, under mild assumptions, to more general models of temporal cliques where the labels may not be locally unique and a same edge may have several labels. The main open question is now to understand where the separation occurs between graphs that admit sparse spanners and graphs that do not.

Connected through hyperedge HE-1 (induced by source paper "the computational complexity of finding temporal paths under waiting time constraints"):
  with members:
    - "an adaptive version of brandes algorithm for betweenness centrality"
    - "efficient computation of optimal temporal walks under waiting time constraints"
    - "how fast can we reach a target vertex in stochastic temporal graphs"
    - "fixed parameter algorithms for maximum profit facility location under matroid constraints"
    - "the temporal explorer who returns to the base"
    - "changing times to minimise reachability in temporal graphs"
    - "deleting edges to restrict the size of an epidemic in temporal networks"
    - "stream graphs and link streams for the modeling of interactions over time"

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.DM(Discrete Mathematics)