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 "an optimal monotone contention resolution scheme for bipartite matchings via a polyhedral viewpoint"):
  Abstract: Relaxation and rounding approaches became a standard and extremely versatile tool for constrained submodular function maximization. One of the most common rounding techniques in this context are contention resolution schemes. Such schemes round a fractional point by first rounding each coordinate independently, and then dropping some elements to reach a feasible set. Also the second step, where elements are dropped, is typically randomized. This leads to an additional source of randomization within the procedure, which can complicate the analysis. We suggest a different, polyhedral viewpoint to design contention resolution schemes, which avoids to deal explicitly with the randomization in the second step. This is achieved by focusing on the marginals of a dropping procedure. Apart from avoiding one source of randomization, our viewpoint allows for employing polyhedral techniques. Both can significantly simplify the construction and analysis of contention resolution schemes. We show how, through our framework, one can obtain an optimal monotone contention resolution scheme for bipartite matchings. So far, only very few results are known about optimality of monotone contention resolution schemes. Our contention resolution scheme for the bipartite case also improves the lower bound on the correlation gap for bipartite matchings. Furthermore, we derive a monotone contention resolution scheme for matchings that significantly improves over the previously best one. At the same time, our scheme implies that the currently best lower bound on the correlation gap for matchings is not tight. Our results lead to improved approximation factors for various constrained submodular function maximization problems over a combination of matching constraints with further constraints.

Includes members:
  Member "understanding the correlation gap for matchings":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "optimal online contention resolution schemes via ex ante prophet inequalities")
  Member "submodular maximization through the lens of linear programming":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "algorithmic meta theorems for monotone submodular maximization")
  Member "greed is good near optimal submodular maximization via greedy optimization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "regularized submodular maximization at scale")
  Member "constrained submodular maximization beyond 1 e":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "optimal streaming algorithms for submodular maximization with cardinality constraints")
  Member "online contention resolution schemes":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "prophet inequalities with linear correlations and augmentations")
  Member "non negative submodular stochastic probing via stochastic contention resolution schemes":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "submodular stochastic probing with prices")
  Member "a stochastic probing problem with applications":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning optimal search algorithms from data")
  Member "a k 3 2 approximation algorithm for monotone submodular k set packing and general k exchange systems":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "almost optimal semi streaming maximization for k extendible systems")

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)