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: "joint energy and sinr coverage in spatially clustered rf powered iot network"
  Abstract: Owing to the ubiquitous availability of radio-frequency (RF) signals, RF energy harvesting is emerging as an appealing solution for powering Internet-of-Things (IoT) devices. In this paper, we model and analyze an IoT network which harvests RF energy and receives information from the same wireless network. In order to enable this operation, each time slot is partitioned into  charging  and  information reception  phases. For this setup, we characterize two performance metrics: 1) energy coverage and 2) joint signal-to-interference-plus-noise and energy coverage. The analysis is performed using a realistic spatial model that captures the spatial coupling between the locations of the IoT devices and the nodes of the wireless network (referred, henceforth, as the IoT gateways), which is often ignored in the literature. In particular, we model the locations of the IoT devices using a Poisson cluster process and assume that some of the clusters have IoT gateways (GWs) deployed at their centers while the other GWs are deployed independently of the IoT devices. The level of coupling can be controlled by tuning the fraction of total GWs that are deployed at the cluster centers. Due to the inherent intractability of computing the distribution of shot noise process for this setup, we propose two accurate approximations, using which the aforementioned metrics are characterized. Multiple system design insights are drawn from our results. For instance, we demonstrate the existence of optimal slot partitioning that maximizes the system throughput. In addition, we explore the effect of the level of coupling between the locations of the IoT devices and the GWs on this optimal slot partitioning. Particularly, our results reveal that the optimal value of time duration for the charging phase increases as the level of coupling decreases.

Connected through hyperedge HE-1 (induced by source paper "on the role of age of information in internet of things"):
  with members:
    - "age and value of information non linear age case"
    - "update or wait how to keep your data fresh"
    - "optimizing data freshness throughput and delay in multi server information update systems"
    - "practical non linear energy harvesting model and resource allocation for swipt systems"
    - "on the age of information in status update systems with packet management"
    - "wide area wireless communication challenges for the internet of things"

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.IT(Information Theory)