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 "fully dynamic effective resistances"):
  Abstract: In this paper we consider the \emph{fully-dynamic} All-Pairs Effective Resistance problem, where the goal is to maintain effective resistances on a graph $G$ among any pair of query vertices under an intermixed sequence of edge insertions and deletions in $G$. The effective resistance between a pair of vertices is a physics-motivated quantity that encapsulates both the congestion and the dilation of a flow. It is directly related to random walks, and it has been instrumental in the recent works for designing fast algorithms for combinatorial optimization problems, graph sparsification, and network science. #R##N#We give a data-structure that maintains $(1+\epsilon)$-approximations to all-pair effective resistances of a fully-dynamic unweighted, undirected multi-graph $G$ with $\tilde{O}(m^{4/5}\epsilon^{-4})$ expected amortized update and query time, against an oblivious adversary. Key to our result is the maintenance of a dynamic \emph{Schur complement}~(also known as vertex resistance sparsifier) onto a set of terminal vertices of our choice. #R##N#This maintenance is obtained (1) by interpreting the Schur complement as a sum of random walks and (2) by randomly picking the vertex subset into which the sparsifier is constructed. We can then show that each update in the graph affects a small number of such walks, which in turn leads to our sub-linear update time. We believe that this local representation of vertex sparsifiers may be of independent interest.

Includes members:
  Member "kirchhoff index as a measure of edge centrality in weighted networks nearly linear time algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "fully dynamic spectral vertex sparsifiers and applications")
  Member "density independent algorithms for sparsifying k step random walks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "fully dynamic spectral vertex sparsifiers and applications")
  Member "the power of vertex sparsifiers in dynamic graph algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "fully dynamic spectral vertex sparsifiers and applications")
  Member "dynamic minimum spanning forest with subpolynomial worst case update time":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "dynamic graph algorithms with batch updates in the massively parallel computation model")
  Member "fully dynamic maximal matching in constant update time":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "dynamic graph algorithms with batch updates in the massively parallel computation model")
  Member "fully dynamic all pairs shortest paths with worst case update time revisited":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "approximate distance oracles subject to multiple vertex failures")
  Member "incremental exact min cut in poly logarithmic amortized update time":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "fully dynamic spectral vertex sparsifiers and applications")
  Member "faster fully dynamic minimum spanning forest":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "work efficient batch incremental minimum spanning trees with applications to the sliding window model")

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)