human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "dynamic low stretch trees via dynamic low diameter decompositions"
  Abstract: Spanning trees of low average stretch on the non-tree edges, as introduced by Alon et al. [SICOMP 1995], are a natural graph-theoretic object. In recent years, they have found significant applications in solvers for symmetric diagonally dominant (SDD) linear systems. In this work, we provide the first dynamic algorithm for maintaining such trees under edge insertions and deletions to the input graph. Our algorithm has update time n1/2 + o(1) and the average stretch of the maintained tree is no(1) , which matches the stretch in the seminal result of Alon et al.    Similar to Alon et al., our dynamic low-stretch tree algorithm employs a dynamic hierarchy of low-diameter decompositions (LDDs). As a major building block we use a dynamic LDD that we obtain by adapting the random-shift clustering of Miller et al. [SPAA 2013] to the dynamic setting.    The major technical challenge in our approach is to control the propagation of updates within our hierarchy of LDDs: each update to one level of the hierarchy could potentially induce several insertions and deletions to the next level of the hierarchy. We achieve this goal by a sophisticated amortization approach. In particular, we give a bound on the number of changes made to the LDD per update to the input graph that is significantly better than the trivial bound implied by the update time.    We believe that the dynamic random-shift clustering might be useful for independent applications. One of these applications is the dynamic spanner problem. By combining the random-shift clustering with the recent spanner construction of Elkin and Neiman [SODA 2017]. We obtain a fully dynamic algorithm for maintaining a spanner of stretch 2k − 1 and size O (n1 + 1/k logn) with amortized update time O (k log2 n) for any integer 2 ≤ k ≤ logn . Compared to the state-of-the art in this regime Baswana et al. [TALG 2012], we improve upon the size of the spanner and the update time by a factor of k .

Connected through hyperedge HE-1 (induced by source paper "practice of streaming and dynamic graphs concepts models systems and parallelism"):
  with members:
    - "storing and querying large scale spatio temporal graphs with high throughput edge insertions"
    - "parallel batch dynamic graph connectivity"
    - "demystifying graph databases analysis and taxonomy of data organization system designs and graph queries"
    - "graph processing on fpgas taxonomy survey challenges"
    - "slim fly a cost effective low diameter network topology"
    - "red blue pebbling revisited near optimal parallel matrix matrix multiplication"
    - "dynamic algorithms for the massively parallel computation model"
    - "fatpaths routing in supercomputers data centers and clouds with low diameter networks when shortest paths fall short"

Connected through hyperedge HE-2 (induced by source paper "fully dynamic spectral vertex sparsifiers and applications"):
  with members:
    - "graph reduction by local variation"
    - "kirchhoff index as a measure of edge centrality in weighted networks nearly linear time algorithms"
    - "dynamic effective resistances and approximate schur complement on separable graphs"
    - "on solving linear systems in sublinear time"
    - "spectrally approximating large graphs with smaller graphs"
    - "an almost linear time algorithm for uniform random spanning tree generation"
    - "density independent algorithms for sparsifying k step random walks"
    - "the power of vertex sparsifiers in dynamic graph algorithms"

Connected through hyperedge HE-3 (induced by source paper "rounding dynamic matchings against an adaptive adversary"):
  with members:
    - "decremental sssp in weighted digraphs faster and against an adaptive adversary"
    - "deterministic algorithms for decremental approximate shortest paths faster and simpler"
    - "tight bounds for online edge coloring"
    - "distributed weighted matching via randomized composable coresets"
    - "online matching with general arrivals"
    - "a new algorithm for decremental single source shortest paths with applications to vertex capacitated flow and cut problems"
    - "towards a unified theory of sparsification for matching problems"
    - "reoptimization via gradual transformations"

Connected through hyperedge HE-4 (induced by source paper "low diameter graph decompositions by approximate distance computation"):
  with members:
    - "a faster distributed single source shortest paths algorithm"
    - "a hierarchy of lower bounds for sublinear additive spanners"
    - "hopsets with constant hopbound and applications to approximate shortest paths"
    - "on the complexity of local distributed graph problems"
    - "parallel metric tree embedding based on an algebraic view on moore bellman ford"
    - "near optimal distributed maximum flow"
    - "simple parallel and distributed algorithms for spectral graph sparsification"
    - "parallel graph decompositions using random shifts"

Connected through hyperedge HE-5 (induced by source paper "fully dynamic k center clustering in doubling metrics"):
  with members:
    - "a tree structure for dynamic facility location"
    - "fully dynamic hierarchical diameter k clustering and k center"
    - "small space stream summary for matroid center"
    - "expander decomposition and pruning faster stronger and simpler"
    - "dynamic clustering to minimize the sum of radii"
    - "the new data and new challenges in multimedia research"
    - "fast construction of nets in low dimensional metrics and their applications"

Question: Please tell me which class the center node 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)