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 "beyond submodular maximization"):
  Abstract: While there are well-developed tools for maximizing a submodular function subject to a matroid constraint, there is much less work on the corresponding supermodular maximization problems. We develop new techniques for attacking these problems inspired by the continuous greedy method applied to the multi-linear extension of a submodular function. We first adapt the continuous greedy algorithm to work for general twice-continuously differentiable functions. The performance of the adapted algorithm depends on a new smoothness parameter. If $F:[0,1]^n\rightarrow\mathbb{R}$ is one-sided $\sigma$-smooth, then the approximation factor only depends on $\sigma$. We apply the new algorithm to a broad class of quadratic supermodular functions arising in diversity maximization. This captures metric diversity maximization ($\sigma=2$) and negative-type diversity ($\sigma=4$). We also develop new methods (inspired by swap rounding and approximate integer decomposition) for rounding quadratics over a matroid polytope. Together with the adapted continuous greedy this leads to a $O(\sigma^{3/2})$-approximation. This is the best asymptotic approximation known for this class of quadratics and the evidence suggests that it may be tight. We then consider general (non-quadratic) functions. We give a broad parameterized family of monotone functions which include submodular functions and the just-discussed supermodular family of discrete quadratics. Such set functions are called $\gamma$-meta-submodular. We develop local search algorithms with approximation factors that depend only on $\gamma$. We show that the $\gamma$-meta-submodular families include well-known function classes including meta-submodular functions ($\gamma=0$), proportionally submodular ($\gamma=1$), and diversity functions based on negative-type distances or Jensen-Shannon divergence (both $\gamma=2$) and (semi-)metric diversity functions.

Includes members:
  Member "distributed maximization of submodular plus diversity functions for multi label feature selection on huge datasets":
    also connected through other hyperedges:
      (none)
  Member "weakly submodular maximization beyond cardinality constraints does randomization help greedy":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "black box submodular maximization discrete and continuous settings")
  Member "online submodular welfare maximization greedy beats 1 2 in random order":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "optimal streaming algorithms for submodular maximization with cardinality constraints")
  Member "guarantees for greedy maximization of non submodular functions with applications":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "optimal approximation for unconstrained non submodular minimization")
  Member "local search for max sum diversification":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a general coreset based approach to diversity maximization under matroid constraints")
  Member "max sum diversification monotone submodular functions and semi metric spaces":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "max sum diversification monotone submodular functions and dynamic updates")
  Member "max sum diversity via convex programming":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "mapreduce and streaming algorithms for diversity maximization in metric spaces of bounded doubling dimension")
  Member "building a good team secretary problems and the supermodular degree":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "an improved algorithm for the submodular secretary problem with a cardinality constraint")

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.DS(Data Structures and Algorithms)