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 "a unified framework of robust submodular optimization"):
  Abstract: In this paper, we shall study a unified framework of robust submodular optimization. We study this problem both from a minimization and maximization perspective (previous work has only focused on variants of robust submodular maximization). We do this under a broad range of combinatorial constraints including cardinality, knapsack, matroid as well as graph based constraints such as cuts, paths, matchings and trees. Furthermore, we also study robust submodular minimization and maximization under multiple submodular upper and lower bound constraints. We show that all these problems are motivated by important machine learning applications including robust data subset selection, robust co-operative cuts and robust co-operative matchings. In each case, we provide scalable approximation algorithms and also study hardness bounds. Finally, we empirically demonstrate the utility of our algorithms on real world applications.

Includes members:
  Member "near optimal algorithms for hard submodular programs with discounted cooperative costs":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "robust submodular minimization with applications to cooperative modeling")
  Member "learning from less data a unified data subset selection and active learning framework for computer vision":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "dropclass and dropadapt dropping classes for deep speaker representation learning")
  Member "robust monotone submodular function maximization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "adversarially robust submodular maximization under knapsack constraints")
  Member "distributionally robust submodular maximization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "mixed strategies for robust optimization of unknown objectives")
  Member "approximating some network problems with scenarios":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "robust submodular minimization with applications to cooperative modeling")
  Member "a framework towards domain specific video summarization":
    also connected through other hyperedges:
      (none)
  Member "robust optimization for non convex objectives":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "distribution agnostic model agnostic meta learning")
  Member "robust submodular maximization offline and online algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "robust submodular minimization with applications to cooperative modeling")

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)