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: "computing exact minimum cuts without knowing the graph"
  Abstract: We give query-efficient algorithms for the global min-cut and the s-t cut problem in unweighted, undirected graphs. Our oracle model is inspired by the submodular function minimization problem: on query $S \subset V$, the oracle returns the size of the cut between $S$ and $V \setminus S$. #R##N#We provide algorithms computing an exact minimum $s$-$t$ cut in $G$ with $\tilde{O}(n^{5/3})$ queries, and computing an exact global minimum cut of $G$ with only $\tilde{O}(n)$ queries (while learning the graph requires $\tilde{\Theta}(n^2)$ queries).

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"
    - "on estimating maximum matching size in graph streams"

Connected through hyperedge HE-2 (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-3 (induced by source paper "deterministic graph cuts in subquadratic time sparse balanced and k vertex"):
  with members:
    - "a faster local algorithm for detecting bounded size cuts with applications to higher connectivity problems"
    - "computing and testing small vertex connectivity in near linear time and queries"
    - "a deterministic algorithm for balanced cut with applications to dynamic connectivity flows and beyond"
    - "distributed edge connectivity in sublinear time"
    - "breaking quadratic time for small vertex connectivity and an approximation scheme"
    - "graph sketching against adaptive adversaries applied to the minimum degree algorithm"
    - "expander decomposition and pruning faster stronger and simpler"
    - "faster algorithms for computing maximal 2 connected subgraphs in sparse directed graphs"

Connected through hyperedge HE-4 (induced by source paper "weighted min cut sequential cut query and streaming algorithms"):
  with members:
    - "polynomial pass lower bounds for graph streaming algorithms"
    - "a simple algorithm for minimum cuts in near linear time"
    - "faster algorithms for edge connectivity via random 2 out contractions"
    - "local flow partitioning for faster edge connectivity"
    - "deterministic edge connectivity in near linear time"
    - "single pass spectral sparsification in dynamic streams"
    - "single pass sparsification in the streaming model with edge deletions"
    - "minimum cuts in near linear time"

Connected through hyperedge HE-5 (induced by source paper "inner product oracle can estimate and sample"):
  with members:
    - "sublinear time numerical linear algebra for structured matrices"
    - "fine grained reductions from approximate counting to decision"
    - "testing matrix rank optimally"
    - "edge estimation with independent set oracles"
    - "on sampling edges almost uniformly"
    - "approximately counting triangles in sublinear time"
    - "the power of an example hidden set size approximation using group queries and conditional sampling"

Connected through hyperedge HE-6 (induced by source paper "hyperedge estimation using polylogarithmic subset queries"):
  with members:
    - "parameterized query complexity of hitting set using stability of sunflowers"
    - "fine grained reductions from approximate counting to decision"
    - "edge estimation with independent set oracles"
    - "on approximating the number of k cliques in sublinear time"
    - "approximately counting triangles in sublinear time"
    - "the power of an example hidden set size approximation using group queries and conditional sampling"
    - "finding cycles and trees in sublinear time"

Connected through hyperedge HE-7 (induced by source paper "new query lower bounds for submodular function minimization"):
  with members:
    - "a faster cutting plane method and its implications for combinatorial and convex optimization"

Connected through hyperedge HE-8 (induced by source paper "parameterized query complexity of hitting set using stability of sunflowers"):
  with members:
    - "set cover in sub linear time"
    - "edge estimation with independent set oracles"
    - "a near optimal sublinear time algorithm for approximating the minimum vertex cover size"

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)