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 "slack and margin rescaling as convex extensions of supermodular functions"):
  Abstract: Slack and margin rescaling are variants of the structured output SVM. They define convex surrogates to task specific loss functions, which, when specialized to non-additive loss functions for multi-label problems, yield extensions to increasing set functions. We demonstrate in this paper that we may use these concepts to define polynomial time convex extensions of arbitrary supermodular functions. We further show that slack and margin rescaling can be interpreted as dominating convex extensions over multiplicative and additive families, and that margin rescaling is strictly dominated by slack rescaling. However, we also demonstrate that, while the function value and gradient for margin rescaling can be computed in polynomial time, the same for slack rescaling corresponds to a non-supermodular maximization problem.

Includes members:
  Member "fast and scalable structural svm with slack rescaling":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "efficient structured surrogate loss and regularization in structured prediction")
  Member "polyhedral aspects of submodularity convexity and concavity":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "robust submodular minimization with applications to cooperative modeling")
  Member "curvature and optimal algorithms for learning and minimizing submodular functions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on the unreasonable effectiveness of the greedy algorithm greedy adapts to sharpness")
  Member "algorithms for approximate minimization of the difference between submodular functions with applications":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "optimal approximation for unconstrained non submodular minimization")

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)