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: "energy efficient iot virtualization framework with peer to peer networking and processing"
  Abstract: In this paper, an energy efficient IoT virtualization framework with P2P networking and edge computing is proposed. In this network, the IoT task processing requests are served by peers. The peers in our work are represented by IoT objects and relays that host virtual machines (VMs). We have considered three scenarios to investigate the saving in power consumption and the system capabilities in terms of task processing. The first scenario is the relays only scenario, where the task requests are processed using relays only. The second scenario is the objects only scenario, where the task requests are processed using the IoT objects only. The last scenario is a hybrid scenario, where the task requests are processed using both IoT objects and VMs. We have developed a mixed integer linear programming (MILP) model to maximize the number of processing tasks served by the system and minimize the total power consumed by the IoT network. We investigated our framework under the impact of VMs placement constraints, fairness constraints between the objects, tasks number limitations, uplink and downlink limited capacities, and processing capability limitations. Based on the MILP model principles, we developed an energy efficient virtualized IoT P2P networks heuristic (EEVIPN). The heuristic results were comparable to those of the MILP in terms of energy efficiency and tasks processing. Our results show that the hybrid scenario serves up to 77% (57% on average) processing task requests, but with higher energy consumption compared to the other scenarios. The relays only scenario can serve 74% (57% on average) of the processing task requests with 8% saving in power consumption compared to the hybrid scenario. In contrast, 28% (22% on average) of task requests can be successfully handled by applying the objects only scenario with up to 62% power saving compared to the hybrid scenario.

Connected through hyperedge HE-1 (induced by source paper "patient centric hetnets powered by machine learning and big data analytics for 6g networks"):
  with members:
    - "using machine learning and big data analytics to prioritize outpatients in hetnets"
    - "service embedding in iot networks"
    - "big data analytics for wireless and wired network design a survey"
    - "patient centric cellular networks optimization using big data analytics"
    - "human in the loop wireless communications machine learning and brain aware resource management"
    - "optimized energy aware 5g network function virtualization"
    - "stacking classifiers for anti spam filtering of e mail"

Connected through hyperedge HE-2 (induced by source paper "energy efficient distributed processing for iot"):
  with members:
    - "all one needs to know about fog computing and related edge computing paradigms a complete survey"
    - "service embedding in iot networks"
    - "patient centric cellular networks optimization using big data analytics"
    - "optimized energy aware 5g network function virtualization"
    - "allocation of heterogeneous resources of an iot device to flexible services"

Connected through hyperedge HE-3 (induced by source paper "energy efficient virtual machines placement over cloud fog network architecture"):
  with members:
    - "big data analytics for wireless and wired network design a survey"
    - "patient centric cellular networks optimization using big data analytics"
    - "bounds for energy efficient survivable ip over wdm networks with network coding"
    - "optimized energy aware 5g network function virtualization"

Connected through hyperedge HE-4 (induced by source paper "a survey of big data machine learning applications optimization in cloud data centers and networks"):
  with members:
    - "fog assisted caching employing solar renewable energy for delivering video on demand service"
    - "energy efficient and resilient infrastructure for fog computing health monitoring applications"
    - "virtual machines embedding for cloud pon awgr and server based data centres"
    - "a survey on geographically distributed big data processing using mapreduce"
    - "disaggregation for improved efficiency in fog computing era"
    - "experimental evaluation of server centric passive optical network based data centre architecture"
    - "energy efficient distributed processing in vehicular cloud architecture"
    - "energy efficient software matching in vehicular fog"

Connected through hyperedge HE-5 (induced by source paper "energy efficient routing and network coding in core networks"):
  with members:
    - "big data analytics for wireless and wired network design a survey"
    - "patient centric cellular networks optimization using big data analytics"
    - "bounds for energy efficient survivable ip over wdm networks with network coding"
    - "optimized energy aware 5g network function virtualization"
    - "optimization based linear network coding for general connections of continuous flows"
    - "overlay protection against link failures using network coding"
    - "network protection codes against link failures using network coding"
    - "genetic representations for evolutionary minimization of network coding resources"

Connected through hyperedge HE-6 (induced by source paper "resilient service embedding in iot networks"):
  with members:
    - "service embedding in iot networks"
    - "big data analytics for wireless and wired network design a survey"
    - "patient centric cellular networks optimization using big data analytics"
    - "bounds for energy efficient survivable ip over wdm networks with network coding"
    - "energy efficiency of server centric pon data center architecture for fog computing"
    - "optimized energy aware 5g network function virtualization"

Connected through hyperedge HE-7 (induced by source paper "impact of the net neutrality repeal on communication networks"):
  with members:
    - "patient centric cellular networks optimization using big data analytics"
    - "bounds for energy efficient survivable ip over wdm networks with network coding"
    - "optimized energy aware 5g network function virtualization"
    - "traffic profiling for mobile video streaming"

Connected through hyperedge HE-8 (induced by source paper "service embedding in iot networks"):
  with members:
    - "big data analytics for wireless and wired network design a survey"
    - "bounds for energy efficient survivable ip over wdm networks with network coding"
    - "energy efficiency of server centric pon data center architecture for fog computing"
    - "optimized energy aware 5g network function virtualization"

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)