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 graph stream algorithms in o n space"
  Abstract: In this paper we study graph problems in dynamic streaming model, where the input is defined by a sequence of edge insertions and deletions. As many natural problems require $\Omega(n)$ space, where $n$ is the number of vertices, existing works mainly focused on designing $\tilde{O}(n)$ space algorithms. Although sublinear in the number of edges for dense graphs, it could still be too large for many applications (e.g. $n$ is huge or the graph is sparse). In this work, we give single-pass algorithms beating this space barrier for two classes of problems. #R##N#We present $o(n)$ space algorithms for estimating the number of connected components with additive error $\varepsilon n$ and $(1+\varepsilon)$-approximating the weight of minimum spanning tree, for any small constant $\varepsilon>0$. The latter improves previous $\tilde{O}(n)$ space algorithm given by Ahn et al. (SODA 2012) for connected graphs with bounded edge weights. #R##N#We initiate the study of approximate graph property testing in the dynamic streaming model, where we want to distinguish graphs satisfying the property from graphs that are $\varepsilon$-far from having the property. We consider the problem of testing $k$-edge connectivity, $k$-vertex connectivity, cycle-freeness and bipartiteness (of planar graphs), for which, we provide algorithms using roughly $\tilde{O}(n^{1-\varepsilon})$ space, which is $o(n)$ for any constant $\varepsilon$. #R##N#To complement our algorithms, we present $\Omega(n^{1-O(\varepsilon)})$ space lower bounds for these problems, which show that such a dependence on $\varepsilon$ is necessary.

Connected through hyperedge HE-1 (induced by source paper "testable properties in general graphs and random order streaming"):
  with members:
    - "a characterization of graph properties testable for general planar graphs with one sided error it is all about forbidden subgraphs"
    - "estimating graph parameters from random order streams"
    - "testable bounded degree graph properties are random order streamable"
    - "on estimating maximum matching size in graph streams"
    - "testing forest isomorphism in the adjacency list model"
    - "planar graphs random walks and bipartiteness testing"

Connected through hyperedge HE-2 (induced by source paper "graph sketching based space efficient data clustering"):
  with members:
    - "space and time efficient algorithm for maintaining dense subgraphs on one pass dynamic streams"
    - "breaking the small cluster barrier of graph clustering"
    - "clustering partially observed graphs via convex optimization"

Connected through hyperedge HE-3 (induced by source paper "on the multiparty communication complexity of testing triangle freeness"):
  with members:
    - "further algebraic algorithms in the congested clique model and applications to graph theoretic problems"
    - "distributed testing of excluded subgraphs"
    - "a hybrid sampling scheme for triangle counting"
    - "fast distributed algorithms for testing graph properties"
    - "approximately counting triangles in sublinear time"
    - "algebraic methods in the congested clique"
    - "optimal principal component analysis in distributed and streaming models"
    - "communication lower bounds for statistical estimation problems via a distributed data processing inequality"

Connected through hyperedge HE-4 (induced by source paper "estimating graph parameters from random order streams"):
  with members:
    - "testable bounded degree graph properties are random order streamable"
    - "the sparse awakens streaming algorithms for matching size estimation in sparse graphs"
    - "a note on logarithmic space stream algorithms for matchings in low arboricity graphs"
    - "sublinear estimation of weighted matchings in dynamic data streams"
    - "a quasi polynomial time partition oracle for graphs with an excluded minor"
    - "a near optimal sublinear time algorithm for approximating the minimum vertex cover size"

Connected through hyperedge HE-5 (induced by source paper "testable bounded degree graph properties are random order streamable"):
  with members:
    - "on estimating maximum matching size in graph streams"
    - "sublinear estimation of weighted matchings in dynamic data streams"
    - "streaming lower bounds for approximating max cut"
    - "constant time algorithms for sparsity matroids"
    - "a near optimal sublinear time algorithm for approximating the minimum vertex cover size"

Connected through hyperedge HE-6 (induced by source paper "near optimal parallel algorithms for dynamic dfs in undirected graphs"):
  with members:
    - "depth first search in the semi streaming model"
    - "dynamic dfs in undirected graphs breaking the o m barrier"
    - "construction and impromptu repair of an mst in a distributed network with o m communication"
    - "space and time efficient algorithm for maintaining dense subgraphs on one pass dynamic streams"
    - "single pass spectral sparsification in dynamic streams"

Connected through hyperedge HE-7 (induced by source paper "distributed property testing for subgraph freeness revisited"):
  with members:
    - "further algebraic algorithms in the congested clique model and applications to graph theoretic problems"
    - "algebraic methods in the congested clique"
    - "tri tri again finding triangles and small subgraphs in a distributed setting"

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)