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: "almost optimal streaming algorithms for coverage problems"
  Abstract: Maximum coverage and minimum set cover problems --collectively called coverage problems-- have been studied extensively in streaming models. However, previous research not only achieve sub-optimal approximation factors and space complexities, but also study a restricted set arrival model which makes an explicit or implicit assumption on oracle access to the sets, ignoring the complexity of reading and storing the whole set at once. In this paper, we address the above shortcomings, and present algorithms with improved approximation factor and improved space complexity, and prove that our results are almost tight. Moreover, unlike most of previous work, our results hold on a more general edge arrival model. More specifically, we present (almost) optimal approximation algorithms for maximum coverage and minimum set cover problems in the streaming model with an (almost) optimal space complexity of $\tilde{O}(n)$, i.e., the space is {\em independent of the size of the sets or the size of the ground set of elements}. These results not only improve over the best known algorithms for the set arrival model, but also are the first such algorithms for the more powerful {\em edge arrival} model. In order to achieve the above results, we introduce a new general sketching technique for coverage functions: This sketching scheme can be applied to convert an $\alpha$-approximation algorithm for a coverage problem to a $(1-\eps)\alpha$-approximation algorithm for the same problem in streaming, or RAM models. We show the significance of our sketching technique by ruling out the possibility of solving coverage problems via accessing (as a black box) a $(1 \pm \eps)$-approximate oracle (e.g., a sketch function) that estimates the coverage function on any subfamily of the sets.

Connected through hyperedge HE-1 (induced by source paper "approximability of monotone submodular function maximization under cardinality and matroid constraints in the streaming model"):
  with members:
    - "submodular streaming in all its glory tight approximation minimum memory and low adaptive complexity"
    - "an exponential speedup in parallel running time for submodular maximization without loss in approximation"
    - "multi pass streaming algorithms for monotone submodular function maximization"
    - "towards nearly linear time algorithms for submodular maximization with a matroid constraint"
    - "submodular function maximization in parallel via the multilinear relaxation"
    - "beyond 1 2 approximation for submodular maximization on massive data streams"
    - "submodular maximization with nearly optimal approximation and adaptivity in nearly linear time"
    - "a nearly linear time algorithm for submodular maximization with a knapsack constraint"

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 "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"
    - "tight bounds for single pass streaming complexity of the set cover problem"
    - "towards tight bounds for the streaming 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-4 (induced by source paper "seeding with costly network information"):
  with members:
    - "beyond worst case in approximability of nonsubmodular influence maximization"
    - "metric sublinear algorithms via linear sampling"
    - "the importance of communities for learning to influence"
    - "stochastic submodular maximization the case of coverage functions"
    - "online influence maximization under independent cascade model with semi bandit feedback"
    - "robust influence maximization"
    - "online influence maximization"
    - "scalable methods for adaptively seeding a social network"

Connected through hyperedge HE-5 (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"
    - "tight bounds for single pass streaming complexity of the set cover problem"
    - "towards tight bounds for the streaming 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-6 (induced by source paper "set cover in sub linear time"):
  with members:
    - "tight bounds for single pass streaming complexity of the set cover problem"
    - "towards tight bounds for the streaming 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-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"
    - "better streaming algorithms for the maximum coverage problem"
    - "submodular maximization over sliding windows"

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"
    - "towards tight bounds for the streaming set cover problem"
    - "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)