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 "learning mixtures of submodular shells with application to document summarization"):
  Abstract: We introduce a method to learn a mixture of submodular "shells" in a large-margin setting. A submodular shell is an abstract submodular function that can be instantiated with a ground set and a set of parameters to produce a submodular function. A mixture of such shells can then also be so instantiated to produce a more complex submodular function. What our algorithm learns are the mixture weights over such shells. We provide a risk bound guarantee when learning in a large-margin structured-prediction setting using a projected subgradient method when only approximate submodular optimization is possible (such as with submodular function maximization). We apply this method to the problem of multi-document summarization and produce the best results reported so far on the widely used NIST DUC-05 through DUC-07 document summarization corpora.

Includes members:
  Member "algorithms for approximate minimization of the difference between submodular functions with applications":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "optimal approximation for unconstrained non submodular minimization")
  Member "learning determinantal point processes":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "improving the similarity measure of determinantal point processes for extractive multi document summarization")
  Member "a submodular supermodular procedure with applications to discriminative structure learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a unified framework of robust submodular optimization")
  Member "pac learning bounded tree width graphical models":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "efficient learning of optimal markov network topology with k tree modeling")
  Member "near optimal nonmyopic value of information in graphical models":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on the unreasonable effectiveness of the greedy algorithm greedy adapts to sharpness")
  Member "efficient minimization of decomposable submodular functions":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "localized flow based clustering in hypergraphs")
  Member "bayesian query focused summarization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "improving the similarity measure of determinantal point processes for extractive multi document summarization")

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)