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 "submodular streaming in all its glory tight approximation minimum memory and low adaptive complexity"):
  Abstract: Streaming algorithms are generally judged by the quality of their solution, memory footprint, and computational complexity. In this paper, we study the problem of maximizing a monotone submodular function in the streaming setting with a cardinality constraint $k$. We first propose Sieve-Streaming++, which requires just one pass over the data, keeps only $O(k)$ elements and achieves the tight $(1/2)$-approximation guarantee. The best previously known streaming algorithms either achieve a suboptimal $(1/4)$-approximation with $\Theta(k)$ memory or the optimal $(1/2)$-approximation with $O(k\log k)$ memory. Next, we show that by buffering a small fraction of the stream and applying a careful filtering procedure, one can heavily reduce the number of adaptive computational rounds, thus substantially lowering the computational complexity of Sieve-Streaming++. We then generalize our results to the more challenging multi-source streaming setting. We show how one can achieve the tight $(1/2)$-approximation guarantee with $O(k)$ shared memory while minimizing not only the required rounds of computations but also the total number of communicated bits. Finally, we demonstrate the efficiency of our algorithms on real-world data summarization tasks for multi-source streams of tweets and of YouTube videos.

Includes members:
  Member "an exponential speedup in parallel running time for submodular maximization without loss in approximation":
    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 "data summarization at scale a two stage submodular approach":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "regularized submodular maximization at scale")
  Member "submodular maximization with optimal approximation adaptivity and query complexity":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a polynomial lower bound on adaptive complexity of submodular maximization")
  Member "unconstrained submodular maximization with constant adaptive complexity":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a polynomial lower bound on adaptive complexity of submodular maximization")
  Member "do less get more streaming submodular maximization with subsampling":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "regularized submodular maximization at scale")
  Member "parallelizing greedy for submodular set function maximization in matroids and beyond":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a polynomial lower bound on adaptive complexity of submodular maximization")
  Member "non monotone submodular maximization in exponentially fewer iterations":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a polynomial lower bound on adaptive complexity of submodular maximization")
  Member "beyond 1 2 approximation for submodular maximization on massive data streams":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "regularized submodular maximization at scale")

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)