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: "estimating graph parameters from random order streams"
  Abstract: We develop a new algorithmic technique that allows to transfer some constant time approximation algorithms for general graphs into random order streaming algorithms. We illustrate our technique by proving that in random order streams with probability at least $2/3$, #R##N#$\bullet$ the number of connected components of $G$ can be approximated up to an additive error of $\varepsilon n$ using $(\frac{1}{\varepsilon})^{O(1/\varepsilon^3)}$ space, #R##N#$\bullet$ the weight of a minimum spanning tree of a connected input graph with integer edges weights from $\{1,\dots,W\}$ can be approximated within a multiplicative factor of $1+\varepsilon$ using $\big(\frac{1}{\varepsilon}\big)^{\tilde O(W^3/\varepsilon^3)}$ space, #R##N#$\bullet$ the size of a maximum independent set in planar graphs can be approximated within a multiplicative factor of $1+\varepsilon$ using space $2^{(1/\varepsilon)^{(1/\varepsilon)^{\log^{O(1)} (1/\varepsilon)}}}$.

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 "space efficient approximation to maximum matching size from uniform edge samples"):
  with members:
    - "sparsifying distributed algorithms with ramifications in massively parallel computation and centralized local computation"
    - "weighted matchings via unweighted augmentations"
    - "coresets meet edcs algorithms for matching and vertex cover on massive graphs"
    - "extractor based time space lower bounds for learning"
    - "testable bounded degree graph properties are random order streamable"
    - "on estimating maximum matching size in graph streams"
    - "a 2 ϵ approximation for maximum weight matching in the semi streaming model"
    - "the sparse awakens streaming algorithms for matching size estimation in sparse graphs"

Connected through hyperedge HE-3 (induced by source paper "constant time dynamic delta 1 coloring and weight approximation for minimum spanning forest dynamic algorithms meet property testing"):
  with members:
    - "testable properties in general graphs and random order streaming"
    - "improved dynamic graph coloring"
    - "testable bounded degree graph properties are random order streamable"
    - "dynamic algorithms for graph coloring"
    - "dynamic graph coloring"
    - "dynamic minimum spanning forest with subpolynomial worst case update time"
    - "fully dynamic maximal matching in constant update time"
    - "optimal dynamic distributed mis"

Connected through hyperedge HE-4 (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"
    - "testable bounded degree graph properties are random order streamable"
    - "on estimating maximum matching size in graph streams"
    - "dynamic graph stream algorithms in o n space"
    - "testing forest isomorphism in the adjacency list model"
    - "planar graphs random walks and bipartiteness testing"

Connected through hyperedge HE-5 (induced by source paper "the sketching complexity of graph and hypergraph counting"):
  with members:
    - "size bounds and query plans for relational joins"
    - "on estimating maximum matching size in graph streams"
    - "on the multiparty communication complexity of testing triangle freeness"
    - "escape efficiently counting all 5 vertex subgraphs"
    - "on approximating functions of the singular values in a stream"
    - "kernelization via sampling with applications to dynamic graph streams"
    - "tight bounds for linear sketches of approximate matchings"
    - "approximately counting triangles in sublinear time"

Connected through hyperedge HE-6 (induced by source paper "an optimal space lower bound for approximating max cut"):
  with members:
    - "the sketching complexity of graph and hypergraph counting"
    - "on estimating maximum matching size in graph streams"
    - "sublinear estimation of weighted matchings in dynamic data streams"
    - "maximum matching in turnstile streams"
    - "sketching cuts in graphs and hypergraphs"
    - "streaming lower bounds for approximating max cut"
    - "single pass spectral sparsification in dynamic streams"
    - "access to data and number of iterations dual primal algorithms for maximum matching under resource constraints"

Connected through hyperedge HE-7 (induced by source paper "testing graph clusterability algorithms and lower bounds"):
  with members:
    - "testing bounded arboricity"
    - "on approximating the number of k cliques in sublinear time"
    - "on sampling edges almost uniformly"
    - "sublinear time estimation of degree distribution moments the degeneracy connection"
    - "testing cluster structure of graphs"
    - "approximately counting triangles in sublinear time"
    - "a simpler sublinear algorithm for approximating the triangle count"
    - "partitioning into expanders"

Connected through hyperedge HE-8 (induced by source paper "dynamic graph stream algorithms in o n space"):
  with members:
    - "correlation clustering in data streams"
    - "kernelization via sampling with applications to dynamic graph streams"
    - "densest subgraph in dynamic graph streams"
    - "sublinear estimation of weighted matchings in dynamic data streams"
    - "maximum matching in turnstile streams"
    - "applications of uniform sampling densest subgraph and beyond"
    - "space and time efficient algorithm for maintaining dense subgraphs on one pass dynamic streams"
    - "sketching cuts in graphs and hypergraphs"

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)