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 "regularized submodular maximization at scale"):
  Abstract: In this paper, we propose scalable methods for maximizing a regularized submodular function $f = g - \ell$ expressed as the difference between a monotone submodular function $g$ and a modular function $\ell$. Indeed, submodularity is inherently related to the notions of diversity, coverage, and representativeness. In particular, finding the mode of many popular probabilistic models of diversity, such as determinantal point processes, submodular probabilistic models, and strongly log-concave distributions, involves maximization of (regularized) submodular functions. Since a regularized function $f$ can potentially take on negative values, the classic theory of submodular maximization, which heavily relies on the non-negativity assumption of submodular functions, may not be applicable. To circumvent this challenge, we develop the first one-pass streaming algorithm for maximizing a regularized submodular function subject to a $k$-cardinality constraint. It returns a solution $S$ with the guarantee that $f(S)\geq(\phi^{-2}-\epsilon) \cdot g(OPT)-\ell (OPT)$, where $\phi$ is the golden ratio. Furthermore, we develop the first distributed algorithm that returns a solution $S$ with the guarantee that $\mathbb{E}[f(S)] \geq (1-\epsilon) [(1-e^{-1}) \cdot g(OPT)-\ell(OPT)]$ in $O(1/ \epsilon)$ rounds of MapReduce computation, without keeping multiple copies of the entire dataset in each round (as it is usually done). We should highlight that our result, even for the unregularized case where the modular term $\ell$ is zero, improves the memory and communication complexity of the existing work by a factor of $O(1/ \epsilon)$ while arguably provides a simpler distributed algorithm and a unifying analysis. We also empirically study the performance of our scalable methods on a set of real-life applications, including finding the mode of distributions, data summarization, and product recommendation.

Includes members:
  Member "flexible modeling of diversity with strongly log concave distributions":
    also connected through other hyperedges:
      (none)
  Member "submodular streaming in all its glory tight approximation minimum memory and low adaptive complexity":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "approximability of monotone submodular function maximization under cardinality and matroid constraints in the streaming model")
  Member "strong log concavity does not imply log submodularity":
    also connected through other hyperedges:
      (none)
  Member "an optimal streaming algorithm for non monotone submodular maximization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "streaming submodular maximization under a k set system constraint")
  Member "making a sieve random improved semi streaming algorithm for submodular maximization under a cardinality constraint":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "streaming submodular maximization under a k set system constraint")
  Member "submodular maximization beyond non negativity guarantees fast algorithms and applications":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "pandora s box problem with order constraints")
  Member "data summarization at scale a two stage submodular approach":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "streaming submodular maximization under a k set system constraint")
  Member "a tight approximation for submodular maximization with mixed packing and covering constraints":
    also connected through other hyperedges:
      (none)

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)