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: "streaming lower bounds for approximating max cut"
  Abstract: We consider the problem of estimating the value of max cut in a graph in the streaming model of computation. At one extreme, there is a trivial $2$-approximation for this problem that uses only $O(\log n)$ space, namely, count the number of edges and output half of this value as the estimate for max cut value. On the other extreme, if one allows $\tilde{O}(n)$ space, then a near-optimal solution to the max cut value can be obtained by storing an $\tilde{O}(n)$-size sparsifier that essentially preserves the max cut. An intriguing question is if poly-logarithmic space suffices to obtain a non-trivial approximation to the max-cut value (that is, beating the factor $2$). It was recently shown that the problem of estimating the size of a maximum matching in a graph admits a non-trivial approximation in poly-logarithmic space. #R##N#Our main result is that any streaming algorithm that breaks the $2$-approximation barrier requires $\tilde{\Omega}(\sqrt{n})$ space even if the edges of the input graph are presented in random order. Our result is obtained by exhibiting a distribution over graphs which are either bipartite or $\frac{1}{2}$-far from being bipartite, and establishing that $\tilde{\Omega}(\sqrt{n})$ space is necessary to differentiate between these two cases. Thus as a direct corollary we obtain that $\tilde{\Omega}(\sqrt{n})$ space is also necessary to test if a graph is bipartite or $\frac{1}{2}$-far from being bipartite. #R##N#We also show that for any $\epsilon > 0$, any streaming algorithm that obtains a $(1 + \epsilon)$-approximation to the max cut value when edges arrive in adversarial order requires $n^{1 - O(\epsilon)}$ space, implying that $\Omega(n)$ space is necessary to obtain an arbitrarily good approximation to the max cut value.

Connected through hyperedge HE-1 (induced by source paper "smaller cuts higher lower bounds"):
  with members:
    - "a faster deterministic distributed algorithm for weighted apsp through pipelining"
    - "a deterministic distributed algorithm for exact weighted all pairs shortest paths in tilde o n 3 2 rounds"
    - "distributed exact weighted all pairs shortest paths in near linear time"
    - "optimal distributed coloring algorithms for planar graphs in the local model"
    - "distributed exact shortest paths in sublinear time"
    - "a time hierarchy theorem for the local model"
    - "a 2 ϵ approximation for maximum weight matching in the semi streaming model"
    - "quadratic and near quadratic lower bounds for the congest model"

Connected through hyperedge HE-2 (induced by source paper "the sketching complexity of graph and hypergraph counting"):
  with members:
    - "size bounds and query plans for relational joins"
    - "estimating graph parameters from random order streams"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "metric sublinear algorithms via linear sampling"):
  with members:
    - "sublinear algorithms for maxcut and correlation clustering"
    - "tight space approximation tradeoff for the multi pass streaming set cover problem"
    - "a 2 ϵ approximation for maximum weight matching in the semi streaming model"
    - "distributed coverage maximization via sketching"
    - "almost optimal streaming algorithms for coverage problems"
    - "towards tight bounds for the streaming set cover problem"
    - "densest subgraph in dynamic graph streams"
    - "tight bounds for linear sketches of approximate matchings"

Connected through hyperedge HE-4 (induced by source paper "an optimal space lower bound for approximating max cut"):
  with members:
    - "the sketching complexity of graph and hypergraph counting"
    - "estimating graph parameters from random order streams"
    - "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"
    - "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-5 (induced by source paper "sublinear algorithms for maxcut and correlation clustering"):
  with members:
    - "correlation clustering in data streams"
    - "sub exponential approximation schemes for csps from dense to almost sparse"
    - "sketching cuts in graphs and hypergraphs"
    - "the sketching complexity of graph cuts"
    - "better bounds for matchings in the streaming model"
    - "a note on no need to choose how to get both a ptas and sublinear query complexity"
    - "single pass sparsification in the streaming model with edge deletions"
    - "graph sparsification via refinement sampling"

Connected through hyperedge HE-6 (induced by source paper "fast distributed approximation for max cut"):
  with members:
    - "efficient algorithms for constructing very sparse spanners and emulators"
    - "a distributed 2 epsilon approximation for vertex cover in o log delta epsilon log log delta rounds"
    - "distributed strong diameter network decomposition"
    - "randomized composable core sets for distributed submodular maximization"
    - "a deterministic almost tight distributed algorithm for approximating single source shortest paths"
    - "a new framework for distributed submodular maximization"
    - "large cuts with local algorithms on triangle free graphs"
    - "distributed approximation algorithms for weighted shortest paths"

Connected through hyperedge HE-7 (induced by source paper "testable bounded degree graph properties are random order streamable"):
  with members:
    - "on estimating maximum matching size in graph streams"
    - "dynamic graph stream algorithms in o n space"
    - "sublinear estimation of weighted matchings in dynamic data streams"
    - "constant time algorithms for sparsity matroids"
    - "a near optimal sublinear time algorithm for approximating the minimum vertex cover size"

Connected through hyperedge HE-8 (induced by source paper "streaming verification of graph properties"):
  with members:
    - "bipartite perfect matching is in quasi nc"
    - "rational proofs with multiple provers"
    - "tight bounds for linear sketches of approximate matchings"
    - "sublinear estimation of weighted matchings in dynamic data streams"
    - "maximum matching in turnstile streams"
    - "sketching cuts in graphs and hypergraphs"
    - "an improved interactive streaming algorithm for the distinct elements problem"
    - "semi streaming algorithms for annotated graph streams"

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)