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: "a two prong approach to energy efficient wsns"
  Abstract: Energy neutral operation of WSNs can be achieved by exploiting the idleness of the workload to bring the average power consumption of each node below the harvesting power available. This paper proposes a combination of state-of-the-art low-power design techniques to minimize the local and global impact of the two main activities of each node: sampling and communication. Dynamic power management is adopted to exploit low-power modes during idle periods, while asynchronous wake-up and prediction-based data collection are used to opportunistically activate hardware components and network nodes only when they are strictly required. Furthermore, the concept of "model-based sensing" is introduced to push prediction-based data collection techniques as close as possible to the sensing elements. The results achieved on representative real-world WSN case studies show that the combined benefits of the design techniques adopted is more than linear, providing an overall power reduction of more than three orders of magnitude.

Connected through hyperedge HE-1 (induced by source paper "low power wide area networks an overview"):
  with members:
    - "low power wide area network analysis can lora scale"
    - "a primer on 3gpp narrowband internet of things nb iot"
    - "mitigating inter network interference in lora networks"
    - "understanding the limits of lorawan"
    - "understanding the iot connectivity landscape a contemporary m2m radio technology roadmap"

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)