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 "on notions of distortion and an almost minimum spanning tree with constant average distortion"):
  Abstract: This paper makes two main contributions: The first is the construction of a near-minimum spanning tree with constant average distortion. The second is a general equivalence theorem relating two refined notions of distortion: scaling distortion and prioritized distortion. #R##N#Minimum Spanning Trees of weighted graphs are fundamental objects in numerous applications. In particular in distributed networks, the minimum spanning tree of the network is often used to route messages between network nodes. Unfortunately, while being most efficient in the total cost of connecting all nodes, minimum spanning trees fail miserably in the desired property of approximately preserving distances between pairs. While known lower bounds exclude the possibility of the worst case distortion of a tree being small, it was shown in [ABN15] that there exists a spanning tree with constant average distortion. Yet, the weight of such a tree may be significantly larger than that of the MST. In this paper, we show that any weighted undirected graph admits a {\em spanning tree} whose weight is at most $(1+\rho)$ times that of the MST, providing {\em constant average distortion} $O(1/\rho)$. Our result exhibits the best possible tradeoff of this type. #R##N#This result makes use of a general equivalence theorem relating two recently developed notions of distortion for metric embedding. The first is the notion of scaling distortion, which provides improved distortion for $1-\epsilon$ fractions of the pairs, for all $\epsilon$ simultaneously. A stronger version called coarse scaling distortion, has improved distortion guarantees for the furthest pairs. The second notion is that of prioritized distortion, a property allowing to prioritize the nodes whose associated distortions will be improved. We show that prioritized distortion is essentially equivalent to coarse scaling distortion via a general transformation.

Includes members:
  Member "constructing light spanners deterministically in near linear time":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "new algorithms and hardness for incremental single source shortest paths in directed graphs")
  Member "the greedy spanner is existentially optimal":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a polynomial time algorithm for almost optimal vertex fault tolerant spanners")
  Member "prioritized metric structures and embedding":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "scattering and sparse partitions and their applications")
  Member "nearly tight low stretch spanning trees":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "2 node connectivity network design")
  Member "embedding metrics into ultrametrics and graphs into spanning trees with constant average distortion":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "covering metric spaces by few trees")
  Member "lower stretch spanning trees":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "2 node connectivity 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)