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 "the sketching complexity of graph cuts"):
  Abstract: We study the problem of sketching an input graph, so that given the sketch, one can estimate the weight of any cut in the graph within factor $1+\epsilon$. We present lower and upper bounds on the size of a randomized sketch, focusing on the dependence on the accuracy parameter $\epsilon>0$. #R##N#First, we prove that for every $\epsilon > 1/\sqrt n$, every sketch that succeeds (with constant probability) in estimating the weight of all cuts $(S,\bar S)$ in an $n$-vertex graph (simultaneously), must be of size $\Omega(n/\epsilon^2)$ bits. In the special case where the sketch is itself a weighted graph (which may or may not be a subgraph) and the estimator is the sum of edge weights across the cut in the sketch, i.e., a cut sparsifier, we show the sketch must have $\Omega(n/\epsilon^2)$ edges, which is optimal. Despite the long sequence of work on graph sparsification, no such lower bound was known on the size of a cut sparsifier. #R##N#We then design a randomized sketch that, given $\epsilon\in(0,1)$ and an edge-weighted $n$-vertex graph, produces a sketch of size $\tilde O(n/\epsilon)$ bits, from which the weight of any cut $(S,\bar S)$ can be reported, with high probability, within factor $1+\epsilon$. The previous upper bound is $\tilde O(n/\epsilon^2)$ bits, which follows by storing a cut sparsifier (Bencz{\'u}r and Karger, 1996). To obtain this improvement, we critically use both that the sketch need only be correct on each fixed cut with high probability (rather than on all cuts), and that the estimation procedure of the data structure can be arbitrary (rather than a weighted subgraph). We also show a lower bound of $\Omega(n/\epsilon)$ bits for the space requirement of any data structure achieving this guarantee.

Includes members:
  Member "sketching cuts in graphs and hypergraphs":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "practice of streaming and dynamic graphs concepts models systems and parallelism")
  Member "single pass spectral sparsification in dynamic streams":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "solving tall dense linear programs in nearly linear time")
  Member "distributed computation of large scale graph problems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "equivalence classes and conditional hardness in massively parallel computations")
  Member "when distributed computation is communication expensive":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the communication complexity of optimization")
  Member "single pass sparsification in the streaming model with edge deletions":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "work efficient batch incremental minimum spanning trees with applications to the sliding window model")
  Member "a nearly mlogn time solver for sdd linear systems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "spectral sparsification via bounded independence sampling")
  Member "fast approximation algorithms for cut based problems in undirected graphs":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a deterministic algorithm for balanced cut with applications to dynamic connectivity flows and beyond")
  Member "graph sparsification via refinement sampling":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "sublinear algorithms for maxcut and correlation clustering")

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)