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: "online scaling of nfv service chains across geo distributed datacenters"
  Abstract: Network Function Virtualization (NFV) is an emerging paradigm that turns hardware-dependent implementation of network functions (i.e., middleboxes) into software modules running on virtualized platforms, for significant cost reduction and ease of management. Such virtual network functions (VNFs) commonly constitute service chains, to provide network services that traffic flows need to go through. Efficient deployment of VNFs for network service provisioning is key to realize the NFV goals. Existing efforts on VNF placement mostly deal with offline or one-time placement, ignoring the fundamental, dynamic deployment and scaling need of VNFs to handle practical time-varying traffic volumes. This work investigates dynamic placement of VNF service chains across geo-distributed datacenters to serve flows between dispersed source and destination pairs, for operational cost minimization of the service chain provider over the entire system span. An efficient online algorithm is proposed, which consists of two main components: (1) A regularization-based approach from online learning literature to convert the offline optimal deployment problem into a sequence of one-shot regularized problems, each to be efficiently solved in one time slot; (2) An online dependent rounding scheme to derive feasible integer solutions from the optimal fractional solutions of the one-shot problems, and to guarantee a good competitive ratio of the online algorithm over the entire time span. We verify our online algorithm with solid theoretical analysis and trace-driven simulations under realistic settings.

Connected through hyperedge HE-1 (induced by source paper "qoe management of multimedia streaming services in future networks a tutorial and survey"):
  with members:
    - "5g network slicing using sdn and nfv a survey of taxonomy architectures and future challenges"
    - "a novel qoe aware sdn enabled nfv based management architecture for future multimedia applications on 5g systems"
    - "mptcp meets fec supporting latency sensitive applications over heterogeneous networks"
    - "network slicing for 5g with sdn nfv concepts architectures and challenges"
    - "nfv and sdn key technology enablers for 5g networks"
    - "network slicing to enable scalability and flexibility in 5g mobile networks"
    - "balancing the migration of virtual network functions with replications in data centers"
    - "5g icn delivering icn services over 5g using network slicing"

Connected through hyperedge HE-2 (induced by source paper "a dynamic reliability aware service placement for network function virtualization nfv"):
  with members:
    - "deep q learning for dynamic reliability aware nfv based service provisioning"
    - "multi timescale online optimization of network function virtualization for service chaining"

Connected through hyperedge HE-3 (induced by source paper "deep q learning for dynamic reliability aware nfv based service provisioning"):
  with members:
    - "z torch an automated nfv orchestration and monitoring solution"
    - "network function virtualization state of the art and research challenges"
    - "playing atari with deep reinforcement learning"

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)