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: "service embedding in iot networks"
  Abstract: The Internet of Things is anticipated to participate in the execution of a variety of complex tasks in the near future. IoT objects capable of handling multiple sensing and actuating functions are the cornerstone of smart applications such as smart buildings, smart factories, home automation, and healthcare automation. These smart applications express their demands in terms of high-level requests. These requests are characterised by the different requirements of sensing actuating functions, processing and memory needs, activation zones, latency, etc. In service-oriented architecture-based IoT, application requests are translated into a business process BP workflow. In this study, we model such a BP as a virtual network containing a set of virtual nodes and links connected in a specific topology. These virtual nodes represent the requested processing and location where sensing or actuation are needed. The virtual links capture the requested communication requirements between nodes. In this paper, we introduce a framework, optimised using mixed integer linear programming MILP, that embeds the BPs from the virtual layer into a lower-level implementation at the IoT physical layer. The proposed framework results in a physical plan that optimally allocates the processing needed and provisions the sensing and actuation at the required locations requirements to an appropriate set of IoT nodes. The optimisation goal is to minimise the IoT layer total power consumption and optimise the traffic distribution in a manner that minimises the traffic latency of each IoT node. Our results show that the proposed framework enhances the network performance by reducing the power consumption and latency.

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"
    - "big data analytics for wireless and wired network design a survey"
    - "patient centric cellular networks optimization using big data analytics"
    - "energy efficient iot virtualization framework with peer to peer networking and processing"
    - "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"
    - "patient centric cellular networks optimization using big data analytics"
    - "energy efficient iot virtualization framework with peer to peer networking and processing"
    - "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 "resilient service embedding in iot 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"
    - "energy efficient iot virtualization framework with peer to peer networking and processing"
    - "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)