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: "towards tight bounds for the streaming set cover problem"
  Abstract: We consider the classic Set Cover problem in the data stream model. For n elements and m sets (m ≥ n) we give a O(1/δ)-pass algorithm with a strongly sub-linear ~O(mnδ) space and logarithmic approximation factor. This yields a significant improvement over the earlier algorithm of Demaine et al. [10] that uses exponentially larger number of passes. We complement this result by showing that the tradeoff between the number of passes and space exhibited by our algorithm is tight, at least when the approximation factor is equal to 1. Specifically, we show that any algorithm that computes set cover exactly using ({1 over 2δ}-1) passes must use ~Ω(mnδ) space in the regime of m=O(n). Furthermore, we consider the problem in the geometric setting where the elements are points in R2 and sets are either discs, axis-parallel rectangles, or fat triangles in the plane, and show that our algorithm (with a slight modification) uses the optimal ~O(n) space to find a logarithmic approximation in O(1/δ) passes.   Finally, we show that any randomized one-pass algorithm that distinguishes between covers of size 2 and 3 must use a linear (i.e., Ω(mn)) amount of space. This is the first result showing that a randomized, approximate algorithm cannot achieve a space bound that is sublinear in the input size.   This indicates that using multiple passes might be necessary in order to achieve sub-linear space bounds for this problem while guaranteeing small approximation factors.

Connected through hyperedge HE-1 (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-2 (induced by source paper "approximate mathbb f _2 sketching of valuation functions"):
  with members:
    - "optimality of linear sketching under modular updates"
    - "optimal lower bounds for universal relation and for samplers and finding duplicates in streams"
    - "testing submodularity and other properties of valuation functions"
    - "almost optimal streaming algorithms for coverage problems"
    - "tight bounds for single pass streaming complexity of the set cover problem"
    - "streaming algorithms for submodular function maximization"
    - "incidence geometries and the pass complexity of semi streaming set cover"
    - "tight bounds on low degree spectral concentration of submodular and xos functions"

Connected through hyperedge HE-3 (induced by source paper "improved local computation algorithm for set cover via sparsification"):
  with members:
    - "sparsifying distributed algorithms with ramifications in massively parallel computation and centralized local computation"
    - "round compression for parallel graph algorithms in strongly sublinear space"
    - "tight space approximation tradeoff for the multi pass streaming set cover problem"
    - "almost optimal streaming algorithms for coverage problems"
    - "tight bounds for single pass streaming complexity of the set cover problem"
    - "incidence geometries and the pass complexity of semi streaming set cover"
    - "semi streaming set cover"
    - "a nearly linear time ptas for explicit fractional packing and covering linear programs"

Connected through hyperedge HE-4 (induced by source paper "streaming frequent items with timestamps and detecting large neighborhoods in graph streams"):
  with members:
    - "optimality of linear sketching under modular updates"
    - "independent sets in vertex arrival streams"
    - "tight space approximation tradeoff for the multi pass streaming set cover problem"
    - "tight bounds for single pass streaming complexity of the set cover problem"
    - "maximum matching in turnstile streams"
    - "semi streaming set cover"
    - "tight bounds for lp samplers finding duplicates in streams and related problems"
    - "analysis of a bloom filter algorithm via the supermarket model"

Connected through hyperedge HE-5 (induced by source paper "set cover in sub linear time"):
  with members:
    - "almost optimal streaming algorithms for coverage problems"
    - "tight bounds for single pass streaming complexity of the set cover problem"
    - "randomized composable core sets for distributed submodular maximization"
    - "incidence geometries and the pass complexity of semi streaming set cover"
    - "a nearly linear time ptas for explicit fractional packing and covering linear programs"
    - "analytical approach to parallel repetition"
    - "a near optimal sublinear time algorithm for approximating the minimum vertex cover size"

Connected through hyperedge HE-6 (induced by source paper "towards a theory of parameterized streaming algorithms"):
  with members:
    - "towards tight er bounds for the excluded grid theorem"
    - "the sparse awakens streaming algorithms for matching size estimation in sparse graphs"
    - "tight bounds for single pass streaming complexity of the set cover problem"
    - "polynomial bounds for the grid minor theorem"
    - "directed subset feedback vertex set is fixed parameter tractable"

Connected through hyperedge HE-7 (induced by source paper "tight bounds on the round complexity of the distributed maximum coverage problem"):
  with members:
    - "distributed partial clustering"
    - "tight space approximation tradeoff for the multi pass streaming set cover problem"
    - "on estimating maximum matching size in graph streams"
    - "combinatorial auctions do need modest interaction"
    - "submodular optimization over sliding windows"
    - "distributed coverage maximization via sketching"
    - "almost optimal streaming algorithms for coverage problems"
    - "better streaming algorithms for the maximum coverage problem"

Connected through hyperedge HE-8 (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"
    - "densest subgraph in dynamic graph streams"
    - "tight bounds for linear sketches of approximate matchings"
    - "line large scale information network embedding"

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)