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 "applications of uniform sampling densest subgraph and beyond"):
  Abstract: Recently [Bhattacharya et al., STOC 2015] provide the first non-trivial algorithm for the densest subgraph problem in the streaming model with additions and deletions to its edges, i.e., for dynamic graph streams. They present a $(0.5-\epsilon)$-approximation algorithm using $\tilde{O}(n)$ space, where factors of $\epsilon$ and $\log(n)$ are suppressed in the $\tilde{O}$ notation. However, the update time of this algorithm is large. To remedy this, they also provide a $(0.25-\epsilon)$-approximation algorithm using $\tilde{O}(n)$ space with update time $\tilde{O}(1)$. #R##N#In this paper we improve the algorithms by Bhattacharya et al. by providing a $(1-\epsilon)$-approximation algorithm using $\tilde{O}(n)$ space. Our algorithm is conceptually simple - it samples $\tilde{O}(n)$ edges uniformly at random, and finds the densest subgraph on the sampled graph. We also show how to perform this sampling with update time $\tilde{O}(1)$. In addition to this, we show that given oracle access to the edge set, we can implement our algorithm in time $\tilde{O}(n)$ on a graph in the standard RAM model. To the best of our knowledge this is the fastest $(0.5-\epsilon)$-approximation algorithm for the densest subgraph problem in the RAM model given such oracle access. #R##N#Further, we extend our results to a general class of graph optimization problems that we call heavy subgraph problems. This class contains many interesting problems such as densest subgraph, directed densest subgraph, densest bipartite subgraph, $d$-cut and $d$-heavy connected component. Our result, by characterizing heavy subgraph problems, partially addresses open problem 13 at the IITK Workshop on Algorithms for Data Streams in 2006 regarding the effects of subsampling in this context.

Includes members:
  Member "densest subgraph in dynamic graph streams":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "near optimal fully dynamic densest subgraph")
  Member "streaming lower bounds for approximating max cut":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "smaller cuts higher lower bounds")
  Member "densest subgraph in streaming and mapreduce":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "equivalence classes and conditional hardness in massively parallel computations")
  Member "fast moment estimation in data streams in optimal space":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "solving tall dense linear programs in nearly linear time")
  Member "approximate sparse recovery optimizing time and measurements":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "privacy preserving learning via deep net pruning")

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)