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 "guarantees for greedy maximization of non submodular functions with applications"):
  Abstract: We investigate the performance of the standard Greedy algorithm for cardinality constrained maximization of non-submodular nondecreasing set functions. While there are strong theoretical guarantees on the performance of Greedy for maximizing submodular functions, there are few guarantees for non-submodular ones. However, Greedy enjoys strong empirical performance for many important non-submodular functions, e.g., the Bayesian A-optimality objective in experimental design. We prove theoretical guarantees supporting the empirical performance. Our guarantees are characterized by a combination of the (generalized) curvature $\alpha$ and the submodularity ratio $\gamma$. In particular, we prove that Greedy enjoys a tight approximation guarantee of $\frac{1}{\alpha}(1- e^{-\gamma\alpha})$ for cardinality constrained maximization. In addition, we bound the submodularity ratio and curvature for several important real-world objectives, including the Bayesian A-optimality objective, the determinantal function of a square submatrix and certain linear programs with combinatorial constraints. We experimentally validate our theoretical findings for both synthetic and real-world applications.

Includes members:
  Member "guaranteed non convex optimization submodular maximization over continuous domains":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "online policies for efficient volunteer crowdsourcing")
  Member "greedy column subset selection new bounds and distributed algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "approximating spectral clustering via sampling a review")
  Member "maximizing a monotone submodular function with a bounded curvature under a knapsack constraint":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "approximability of monotone submodular function maximization under cardinality and matroid constraints in the streaming model")
  Member "weakly submodular functions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "beyond submodular maximization")
  Member "on iterative hard thresholding methods for high dimensional m estimation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "soft threshold weight reparameterization for learnable sparsity")
  Member "curvature and optimal algorithms for learning and minimizing submodular functions":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on the unreasonable effectiveness of the greedy algorithm greedy adapts to sharpness")
  Member "optimal approximation for submodular and supermodular optimization with bounded curvature":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "regularized submodular maximization at scale")
  Member "algorithms for approximate minimization of the difference between submodular functions with applications":
    also connected through other hyperedges:
      - Hyperedge HE-8 (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.DM(Discrete Mathematics)