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: "what can wireless cellular technologies do about the upcoming smart metering traffic"
  Abstract: The introduction of smart electricity meters with cellular radio interfaces has placed an additional load on wireless cellular networks. Currently, these meters are designed for low duty cycle billing and occasional system check, which generates low-rate sporadic traffic. As the number of distributed energy resources increases, household power will become more variable and thus unpredictable from the viewpoint of the distribution system operator (DSO). Therefore, it is expected that in the near future there will be an increase in the number of wide area measurement system (WAMS) devices with phasor measurement unit (PMU)-like capabilities in the distribution grid, thus allowing utilities to monitor the low voltage grid quality while providing information required for tighter grid control. From a communication standpoint, the traffic profile will change drastically toward higher data volumes and higher rates per device. In this paper we characterize the current traffic generated by smart electricity meters, and we discuss the potential traffic requirements resulting from the introduction of enhanced smart meters, i.e. meters with PMUlike capabilities. Our study shows how GSM/ GPRS and LTE cellular system performance behaves with current generation and next generation smart meter traffic, where it is clearly seen that the PMU data will seriously challenge these wireless systems. We conclude by highlighting the possible solutions for upgrading the cellular standards, in order to cope with the upcoming smart metering traffic.

Connected through hyperedge HE-1 (induced by source paper "distributed learning for low latency machine type communication in a massive internet of things"):
  with members:
    - "cognitive lpwan towards intelligent wireless services in hybrid low power wide area networks"
    - "label less learning for traffic control in an edge network"
    - "lte lte a random access for massive machine type communications in smart cities"
    - "learning how to communicate in the internet of things finite resources and heterogeneity"
    - "learning with finite memory for machine type communication"
    - "unmanned aerial vehicle with underlaid device to device communications performance and tradeoffs"
    - "reliable reporting for massive m2m communications with periodic resource pooling"
    - "fundamentals of throughput maximization with random arrivals for m2m communications"

Connected through hyperedge HE-2 (induced by source paper "learning how to communicate in the internet of things finite resources and heterogeneity"):
  with members:
    - "efficient deployment of multiple unmanned aerial vehicles for optimal wireless coverage"
    - "learning with finite memory for machine type communication"
    - "unmanned aerial vehicle with underlaid device to device communications performance and tradeoffs"
    - "mobile internet of things can uavs provide an energy efficient mobile architecture"
    - "towards massive ultra reliable and low latency wireless communication with short packets"
    - "wireless communications in the era of big data"
    - "towards massive machine type cellular communications"
    - "towards a consumer centric grid a behavioral perspective"

Connected through hyperedge HE-3 (induced by source paper "maximizing the link throughput between smart meters and aggregators as secondary users under power and outage constraints"):
  with members:
    - "enhanced transmit antenna selection scheme for secure throughput maximization without csi at the transmitter and its applications on smart grids"
    - "on the joint impact of beamwidth and orientation error on throughput in directional wireless poisson networks"
    - "models for the modern power grid"
    - "machine learning in wireless sensor networks algorithms strategies and applications"
    - "throughput analysis of cognitive wireless networks with poisson distributed nodes based on location information"
    - "an overview of the transmission capacity of wireless networks"
    - "the effect of fading channel inversion and threshold scheduling on ad hoc networks"

Connected through hyperedge HE-4 (induced by source paper "learning with finite memory for machine type communication"):
  with members:
    - "fundamentals of throughput maximization with random arrivals for m2m communications"
    - "power efficient system design for cellular based machine to machine communications"
    - "on learning with finite memory"

Connected through hyperedge HE-5 (induced by source paper "assessment of lte wireless access for monitoring of energy distribution in the smart grid"):
  with members:
    - "a tractable model of the lte access reservation procedure for machine type communications"
    - "reengineering gsm gprs towards a dedicated network for massive smart metering"
    - "analysis of the lte access reservation protocol for real time traffic"

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)