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 "non submodular function maximization subject to a matroid constraint with applications"):
  Abstract: The standard greedy algorithm has been recently shown to enjoy approximation guarantees for constrained non-submodular nondecreasing set function maximization. While these recent results allow to better characterize the empirical success of the greedy algorithm, they are only applicable to simple cardinality constraints. In this paper, we study the problem of maximizing a non-submodular nondecreasing set function subject to a general matroid constraint. We first show that the standard greedy algorithm offers an approximation factor of $\frac{0.4 {\gamma}^{2}}{\sqrt{\gamma r} + 1}$, where $\gamma$ is the submodularity ratio of the function and $r$ is the rank of the matroid. Then, we show that the same greedy algorithm offers a constant approximation factor of $(1 + 1/(1-\alpha))^{-1}$, where $\alpha$ is the generalized curvature of the function. In addition, we demonstrate that these approximation guarantees are applicable to several real-world applications in which the submodularity ratio and the generalized curvature can be bounded. Finally, we show that our greedy algorithm does achieve a competitive performance in practice using a variety of experiments on synthetic and real-world data.

Includes members:
  Member "submodular maximization beyond non negativity guarantees fast algorithms and applications":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "regularized submodular maximization at scale")
  Member "steering social activity a stochastic optimal control point of view":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "can a user guess what her followers want")
  Member "equity of attention amortizing individual fairness in rankings":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "fairsight visual analytics for fairness in decision making")
  Member "deep reinforcement learning of marked temporal point processes":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "time2vec learning a vector representation of time")
  Member "fairness of exposure in rankings":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "fairsight visual analytics for fairness in decision making")
  Member "combinatorial penalties which structures are preserved by convex relaxations":
    also connected through other hyperedges:
      (none)
  Member "weakly submodular maximization beyond cardinality constraints does randomization help greedy":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "black box submodular maximization discrete and continuous settings")
  Member "gradient methods for submodular maximization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "black box submodular maximization discrete and continuous settings")

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.SI(Social and Information Networks)