human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "approximate mathbb f _2 sketching of valuation functions"):
  Abstract: We study the problem of constructing a linear sketch of minimum dimension that allows approximation of a given real-valued function $f \colon \mathbb{F}_2^n \rightarrow \mathbb R$ with small expected squared error. We develop a general theory of linear sketching for such functions through which we analyze their dimension for most commonly studied types of valuation functions: additive, budget-additive, coverage, $\alpha$-Lipschitz submodular and matroid rank functions. This gives a characterization of how many bits of information have to be stored about the input $x$ so that one can compute $f$ under additive updates to its coordinates. #R##N#Our results are tight in most cases and we also give extensions to the distributional version of the problem where the input $x \in \mathbb{F}_2^n$ is generated uniformly at random. Using known connections with dynamic streaming algorithms, both upper and lower bounds on dimension obtained in our work extend to the space complexity of algorithms evaluating $f(x)$ under long sequences of additive updates to the input $x$ presented as a stream. Similar results hold for simultaneous communication in a distributed setting.

Includes members:
  Member "optimality of linear sketching under modular updates":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "streaming frequent items with timestamps and detecting large neighborhoods in graph streams")
  Member "optimal lower bounds for universal relation and for samplers and finding duplicates in streams":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "dynamic streaming spectral sparsification in nearly linear time and space")
  Member "testing submodularity and other properties of valuation functions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "supermodular locality sensitive hashes")
  Member "almost optimal streaming algorithms for coverage problems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "approximability of monotone submodular function maximization under cardinality and matroid constraints in the streaming model")
  Member "tight bounds for single pass streaming complexity of the set cover problem":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "polynomial pass lower bounds for graph streaming algorithms")
  Member "towards tight bounds for the streaming set cover problem":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "polynomial pass lower bounds for graph streaming algorithms")
  Member "streaming algorithms for submodular function maximization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "regularized submodular maximization at scale")
  Member "incidence geometries and the pass complexity of semi streaming set cover":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "two player hidden pointer chasing and multi pass lower bounds in turnstile streams")

Question: Please tell me which class the source paper 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)