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 "robust submodular minimization with applications to cooperative modeling"):
  Abstract: Robust Optimization is becoming increasingly important in machine learning applications. This paper studies the problem of robust submodular minimization subject to combinatorial constraints. Constrained Submodular Minimization arises in several applications such as co-operative cuts in image segmentation, co-operative matchings in image correspondence, etc. Many of these models are defined over clusterings of data points (for example pixels in images), and it is important for these models to be robust to perturbations and uncertainty in the data. While several existing papers have studied robust submodular maximization, ours is the first work to study the minimization version under a broad range of combinatorial constraints including cardinality, knapsack, matroid as well as graph-based constraints such as cuts, paths, matchings, and trees. In each case, we provide scalable approximation algorithms and also study hardness bounds. Finally, we empirically demonstrate the utility of our algorithms on synthetic and real-world datasets.

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 "bring your own greedy max near optimal 1 2 approximations for submodular knapsack")
  Member "approximating some network problems with scenarios":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a unified framework of robust submodular optimization")
  Member "robust optimization for non convex objectives":
    also connected through other hyperedges:
      - Hyperedge HE-3 (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-4 (induced by source paper "a unified framework of robust submodular optimization")
  Member "equilibrium computation and robust optimization in zero sum games with submodular structure":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a unified framework of robust submodular optimization")
  Member "polyhedral aspects of submodularity convexity and concavity":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "stochastic submodular maximization the case of coverage functions")
  Member "submodular optimization with submodular cover and submodular knapsack constraints":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a unified framework of robust submodular optimization")
  Member "curvature and optimal algorithms for learning and minimizing submodular functions":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "on the unreasonable effectiveness of the greedy algorithm greedy adapts to sharpness")

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)