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 "stochastic greedy closing the optimality gap in exact weak submodular maximization"):
  Abstract: Many problems in discrete optimization can be formulated as the task of maximizing a monotone and weak submodular function subject to a cardinality constraint. For such problems, a simple greedy algorithm is guaranteed to find a solution with a value no worse than $1-1/e$ of the optimal. Although the computational complexity of Greedy is linear in the size of the data $m$ and cardinality constraint ($k$), even this linear complexity becomes prohibitive for large-scale datasets. Recently, Mirzasoleyman et al. propose a randomized greedy algorithm, Stochastic-Greedy, with the expected worst case approximation ratio of $1-1/e-\epsilon$ where $0<\epsilon<1$ is a parameter controlling the trade-off between complexity and performance. We consider the following question: Given the small $\epsilon$ gap between the worst-case performance guarantees of Stochastic-Greedy and Greedy, can we expect nearly equivalent conditions for the exact identification of the optimal subset? In this paper we show that in general there is an unbounded gap between the exact performance of Stochastic-Greedy and Greedy by considering the problem of sparse support selection. Tropp and Gilbert show Greedy finds the optimal solution with high probability assuming $n=O(k\log\frac{m}{k})$, the information theoretic lowerbound on minimum number of measurements for exact identification of the optimal subset. By contrast, we show that irrespective of the number of measurements, Stochastic-Greedy with overwhelming probability fails to find the optimal subset. We reveal that the failure of Stochastic-Greedy can be circumvented by progressively increasing the size of the stochastic search space. Employing this insight, we present the first sparse support selection algorithm that achieves exact identification of the optimal subset from $O(k\log\frac{m}{k})$ measurements with complexity $\tilde{O}(m)$ for arbitrary sparse vectors.

Includes members:
  Member "submodular maximization with optimal approximation adaptivity and query complexity":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a polynomial lower bound on adaptive complexity of submodular maximization")
  Member "submodular maximization with nearly optimal approximation and adaptivity in nearly linear time":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "approximability of monotone submodular function maximization under cardinality and matroid constraints in the streaming model")
  Member "a randomized greedy algorithm for near optimal sensor scheduling in large scale sensor networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "scheduling observers over a shared channel with hard delivery deadlines")
  Member "guarantees for greedy maximization of non submodular functions with applications":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "optimal approximation for unconstrained non submodular minimization")
  Member "lazier than lazy greedy":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "good feature matching towards accurate robust vo vslam with low latency")
  Member "a remark on the restricted isometry property in orthogonal matching pursuit":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "progressive stochastic greedy sparse reconstruction and support selection")
  Member "active semi supervised learning using submodular functions":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "on the fairness of time critical influence maximization in social networks")
  Member "sparse recovery with orthogonal matching pursuit under rip":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "efficient active learning of sparse halfspaces with arbitrary bounded noise")

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)