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: "on estimating maximum matching size in graph streams"
  Abstract: We study the problem of estimating the maximum matching size in graphs whose edges are revealed in a streaming manner. We consider both insertion-only streams and dynamic streams and present new upper and lower bound results for both models. #R##N#On the upper bound front, we show that an $\alpha$-approximate estimate of the matching size can be computed in dynamic streams using $\widetilde{O}({n^2/\alpha^4})$ space, and in insertion-only streams using $\widetilde{O}(n/\alpha^2)$-space. On the lower bound front, we prove that any $\alpha$-approximation algorithm for estimating matching size in dynamic graph streams requires $\Omega(\sqrt{n}/\alpha^{2.5})$ bits of space, even if the underlying graph is both sparse and has arboricity bounded by $O(\alpha)$. We further improve our lower bound to $\Omega(n/\alpha^2)$ in the case of dense graphs. #R##N#Furthermore, we prove that a $(1+\epsilon)$-approximation to matching size in insertion-only streams requires RS$(n) \cdot n^{1-O(\epsilon)}$ space; here, RS${n}$ denotes the maximum number of edge-disjoint induced matchings of size $\Theta(n)$ in an $n$-vertex graph. It is a major open problem to determine the value of RS$(n)$, and current results leave open the possibility that RS$(n)$ may be as large as $n/\log n$. We also show how to avoid the dependency on the parameter RS$(n)$ in proving lower bound for dynamic streams and present a near-optimal lower bound of $n^{2-O(\epsilon)}$ for $(1+\epsilon)$-approximation in this model. #R##N#Using a well-known connection between matching size and matrix rank, all our lower bounds also hold for the problem of estimating matrix rank. In particular our results imply a near-optimal $n^{2-O(\epsilon)}$ bit lower bound for $(1+\epsilon)$-approximation of matrix ranks for dense matrices in dynamic streams, answering an open question of Li and Woodruff (STOC 2016).

Connected through hyperedge HE-1 (induced by source paper "two player hidden pointer chasing and multi pass lower bounds in turnstile streams"):
  with members:
    - "approximate maximum matching in random streams"
    - "polynomial pass lower bounds for graph streaming algorithms"
    - "dynamic streaming spectral sparsification in nearly linear time and space"
    - "weighted matchings via unweighted augmentations"
    - "tight bounds on the round complexity of the distributed maximum coverage problem"
    - "tight space approximation tradeoff for the multi pass streaming set cover problem"
    - "simplified and space optimal semi streaming for 2 epsilon approximate matching"
    - "a 2 ϵ approximation for maximum weight matching in the semi streaming model"

Connected through hyperedge HE-2 (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-3 (induced by source paper "polynomial pass lower bounds for graph streaming algorithms"):
  with members:
    - "smaller cuts higher lower bounds"
    - "parallelization does not accelerate convex optimization adaptivity lower bounds for non smooth convex minimization"
    - "an exponential speedup in parallel running time for submodular maximization without loss in approximation"
    - "non monotone submodular maximization with nearly optimal adaptivity complexity"
    - "submodular maximization with optimal approximation adaptivity and query complexity"
    - "tight bounds on the round complexity of the distributed maximum coverage problem"
    - "non monotone submodular maximization in exponentially fewer iterations"
    - "submodular maximization with nearly optimal approximation and adaptivity in nearly linear time"

Connected through hyperedge HE-4 (induced by source paper "dynamic streaming spectral sparsification in nearly linear time and space"):
  with members:
    - "faster spectral sparsification in dynamic streams"
    - "optimal lower bounds for distributed and streaming spanning forest computation"
    - "spectral subspace sparsification"
    - "graph sparsification spectral sketches and faster resistance computation via short cycle decompositions"
    - "an sdp based algorithm for linear sized spectral sparsification"
    - "optimal lower bounds for universal relation and for samplers and finding duplicates in streams"
    - "an almost linear time algorithm for uniform random spanning tree generation"
    - "density independent algorithms for sparsifying k step random walks"

Connected through hyperedge HE-5 (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"
    - "estimating graph parameters from random order streams"
    - "testable bounded degree graph properties are random order streamable"
    - "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-6 (induced by source paper "distributed weighted matching via randomized composable coresets"):
  with members:
    - "online matching with general arrivals"
    - "weighted matchings via unweighted augmentations"
    - "submodular optimization in the mapreduce model"
    - "greedy and local ratio algorithms in the mapreduce model"
    - "coresets meet edcs algorithms for matching and vertex cover on massive graphs"
    - "round compression for parallel matching algorithms"
    - "almost optimal stochastic weighted matching with few queries"
    - "ignorance is almost bliss near optimal stochastic matching with few queries"

Connected through hyperedge HE-7 (induced by source paper "querying a matrix through matrix vector products"):
  with members:
    - "tight query complexity lower bounds for pca via finite sample deformed wigner law"
    - "improved algorithms for adaptive compressed sensing"
    - "testing matrix rank optimally"
    - "near optimal linear decision trees for k sum and related problems"
    - "embeddings of schatten norms with applications to data streams"
    - "on approximating functions of the singular values in a stream"
    - "matrix norms in data streams faster multi pass and row order"
    - "approximately counting triangles in sublinear time"

Connected through hyperedge HE-8 (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"
    - "dynamic graph stream algorithms in o n space"
    - "testing forest isomorphism in the adjacency list model"
    - "planar graphs random walks and bipartiteness testing"

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)