human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "provably efficient algorithms for joint placement and allocation of virtual network functions"
  Abstract: Network Function Virtualization (NFV) has the potential to significantly reduce the capital and operating expenses, shorten product release cycle, and improve service agility. In this paper, we focus on minimizing the total number of Virtual Network Function (VNF) instances to provide a specific service (possibly at different locations) to all the flows in a network. Certain network security and analytics applications may allow fractional processing of a flow at different nodes (corresponding to datacenters), giving an opportunity for greater optimization of resources. Through a reduction from the set cover problem, we show that this problem is NP-hard and cannot even be approximated within a factor of (1 - o(1)) ln(m) (where m is the number of flows) unless P=NP. Then, we design two simple greedy algorithms and prove that they achieve an approximation ratio of (1 - o(1)) ln(m) + 2, which is asymptotically optimal. For special cases where each node hosts multiple VNF instances (which is typically true in practice), we also show that our greedy algorithms have a constant approximation ratio. Further, for tree topologies we develop an optimal greedy algorithm by exploiting the inherent topological structure. Finally, we conduct extensive numerical experiments to evaluate the performance of our proposed algorithms in various scenarios.

Connected through hyperedge HE-1 (induced by source paper "online joint placement and allocation of virtual network functions with heterogeneous servers"):
  with members:
    - "joint placement and allocation of virtual network functions with budget and capacity constraints"
    - "shortest path and maximum flow problems under service function chaining constraints"
    - "approximate and incremental network function placement"
    - "stratos a network aware orchestration layer for virtual middleboxes in clouds"

Connected through hyperedge HE-2 (induced by source paper "okpi all kpi network slicing through efficient resource allocation"):
  with members:
    - "a utility driven multi queue admission control solution for network slicing"
    - "getting the most out of your vnfs flexible assignment of service priorities in 5g"
    - "joint service placement and request routing in multi cell mobile edge computing networks"
    - "vnf placement and resource allocation for the support of vertical services in 5g networks"
    - "joint service caching and task offloading for mobile edge computing in dense networks"
    - "jasper joint optimization of scaling placement and routing of virtual network services"
    - "z torch an automated nfv orchestration and monitoring solution"
    - "approximate and incremental network function placement"

Connected through hyperedge HE-3 (induced by source paper "placement and allocation of virtual network functions multi dimensional case"):
  with members:
    - "joint placement and allocation of virtual network functions with budget and capacity constraints"
    - "shortest path and maximum flow problems under service function chaining constraints"
    - "approximate and incremental network function placement"
    - "string submodular functions with curvature constraints"
    - "online admission control and embedding of service chains"
    - "competitive and deterministic embeddings of virtual networks"
    - "maximizing sequence submodular functions and its application to online advertising"

Connected through hyperedge HE-4 (induced by source paper "joint placement and allocation of virtual network functions with budget and capacity constraints"):
  with members:
    - "shortest path and maximum flow problems under service function chaining constraints"
    - "towards practical constrained monotone submodular maximization"
    - "optimal dynamic cloud network control"
    - "approximate and incremental network function placement"
    - "online admission control and embedding of service chains"
    - "constrained monotone function maximization and the supermodular degree"

Connected through hyperedge HE-5 (induced by source paper "shortest path and maximum flow problems under service function chaining constraints"):
  with members:
    - "multi commodity flow with in network processing"
    - "optimal virtual network function placement in multi cloud service function chaining architecture"
    - "optimal dynamic cloud network control"

Question: Please tell me which class the center node 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)