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 "sample complexity bounds for influence maximization"):
  Abstract: Influence maximization (IM) is the problem of finding for a given $s\geq 1$ a set $S$ of $|S|=s$ nodes in a network with maximum influence. With stochastic diffusion models, the influence of a set $S$ of seed nodes is defined as the expectation of its reachability over simulations, where each simulation specifies a deterministic reachability function. Two well-studied special cases are the Independent Cascade (IC) and the Linear Threshold (LT) models of Kempe, Kleinberg, and Tardos. The influence function in stochastic diffusion is unbiasedly estimated by averaging reachability values over i.i.d. simulations. We study the IM sample complexity: the number of simulations needed to determine a $(1-\epsilon)$-approximate maximizer with confidence $1-\delta$. Our main result is a surprising upper bound of $O( s \tau \epsilon^{-2} \ln \frac{n}{\delta})$ for a broad class of models that includes IC and LT models and their mixtures, where $n$ is the number of nodes and $\tau$ is the number of diffusion steps. Generally $\tau \ll n$, so this significantly improves over the generic upper bound of $O(s n \epsilon^{-2} \ln \frac{n}{\delta})$. Our sample complexity bounds are derived from novel upper bounds on the variance of the reachability that allow for small relative error for influential sets and additive error when influence is small. Moreover, we provide a data-adaptive method that can detect and utilize fewer simulations on models where it suffices. Finally, we provide an efficient greedy design that computes an $(1-1/e-\epsilon)$-approximate maximizer from simulations and applies to any submodular stochastic diffusion model that satisfies the variance bounds.

Includes members:
  Member "stop and stare optimal sampling algorithms for viral marketing in billion scale networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "time constrained adaptive influence maximization")
  Member "multi objective weighted sampling":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "clustering small samples with quality guarantees adaptivity with one2all pps")
  Member "the limitations of optimization from samples":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "polynomial pass lower bounds for graph streaming algorithms")
  Member "sketch based influence maximization and computation scaling up with guarantees":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "rcelf a residual based approach for influence maximization problem")
  Member "all distances sketches revisited hip estimators for massive graphs analysis":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "sampling sketches for concave sublinear functions of frequencies")
  Member "uncovering the temporal dynamics of diffusion networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep collaborative embedding for information cascade prediction")

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.LG(Machine Learning)