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 "faster spectral sparsification in dynamic streams"):
  Abstract: Graph sketching has emerged as a powerful technique for processing massive graphs that change over time (i.e., are presented as a dynamic stream of edge updates) over the past few years, starting with the work of Ahn, Guha and McGregor (SODA'12) on graph connectivity via sketching. In this paper we consider the problem of designing spectral approximations to graphs, or spectral sparsifiers, using a small number of linear measurements, with the additional constraint that the sketches admit an efficient recovery scheme. #R##N#Prior to our work, sketching algorithms were known with near optimal $\tilde O(n)$ space complexity, but $\Omega(n^2)$ time decoding (brute-force over all potential edges of the input graph), or with subquadratic time, but rather large $\Omega(n^{5/3})$ space complexity (due to their reliance on a rather weak relation between connectivity and effective resistances). In this paper we first show how a simple relation between effective resistances and edge connectivity leads to an improved $\widetilde O(n^{3/2})$ space and time algorithm, which we show is a natural barrier for connectivity based approaches. Our main result then gives the first algorithm that achieves subquadratic recovery time, i.e. avoids brute-force decoding, and at the same time nontrivially uses the effective resistance metric, achieving $n^{1.4+o(1)}$ space and recovery time. #R##N#Our main technical contribution is a novel method for `bucketing' vertices of the input graph into clusters that allows fast recovery of edges of high effective resistance: the buckets are formed by performing ball-carving on the input graph using (an approximation to) its effective resistance metric. We feel that this technique is likely to be of independent interest.

Includes members:
  Member "dynamic streaming spectral sparsification in nearly linear time and space":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "solving tall dense linear programs in nearly linear time")
  Member "spectral subspace sparsification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "dynamic streaming spectral sparsification in nearly linear time and space")
  Member "graph sparsification spectral sketches and faster resistance computation via short cycle decompositions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a deterministic algorithm for balanced cut with applications to dynamic connectivity flows and beyond")
  Member "an sdp based algorithm for linear sized spectral sparsification":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a spectral approach to network design")
  Member "an almost linear time algorithm for uniform random spanning tree generation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "sketching transformed matrices with applications to natural language processing")
  Member "density independent algorithms for sparsifying k step random walks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "fully dynamic spectral vertex sparsifiers and applications")
  Member "graph clustering using effective resistance":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a spectral approach to network design")
  Member "edit distance sketching streaming and document exchange":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "streaming with oracle new streaming algorithms for edit distance and lcs")

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)