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 "joint placement and allocation of virtual network functions with budget and capacity constraints"):
  Abstract: With the advent of Network Function Virtualization (NFV), network services that traditionally run on proprietary dedicated hardware can now be realized using Virtual Network Functions (VNFs) that are hosted on general-purpose commodity hardware. This new network paradigm offers a great flexibility to Internet service providers (ISPs) for efficiently operating their networks (collecting network statistics, enforcing management policies, etc.). However, introducing NFV requires an investment to deploy VNFs at certain network nodes (called VNF-nodes), which has to account for practical constraints such as the deployment budget and the VNF-node capacity. To that end, it is important to design a joint VNF-nodes placement and capacity allocation algorithm that can maximize the total amount of network flows that are fully processed by the VNF-nodes while respecting such practical constraints. In contrast to most prior work that often neglects either the budget constraint or the capacity constraint, we explicitly consider both of them. We prove that accounting for these constraints introduces several new challenges. Specifically, we prove that the studied problem is not only NP-hard but also non-submodular. To address these challenges, we introduce a novel relaxation method such that the objective function of the relaxed placement subproblem becomes submodular. Leveraging this useful submodular property, we propose two algorithms that achieve an approximation ratio of $\frac{1}{2}(1-1/e)$ and $\frac{1}{3}(1-1/e)$ for the original non-relaxed problem, respectively. Finally, we corroborate the effectiveness of the proposed algorithms through extensive evaluations using both trace-driven simulations and simulations based on synthesized network settings.

Includes members:
  Member "shortest path and maximum flow problems under service function chaining constraints":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "online joint placement and allocation of virtual network functions with heterogeneous servers")
  Member "towards practical constrained monotone submodular maximization":
    also connected through other hyperedges:
      (none)
  Member "optimal dynamic cloud network control":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "an end to end performance analysis for service chaining in a virtualized network")
  Member "provably efficient algorithms for joint placement and allocation of virtual network functions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "online joint placement and allocation of virtual network functions with heterogeneous servers")
  Member "approximate and incremental network function placement":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "online joint placement and allocation of virtual network functions with heterogeneous servers")
  Member "online admission control and embedding of service chains":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "robust online composition routing and nf placement for nfv enabled services")
  Member "constrained monotone function maximization and the supermodular degree":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "beyond submodular maximization")

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.NI(Networking and Internet Architecture)