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: "tractable stochastic geometry model for iot access in lte networks"
  Abstract: The Internet of Things (IoT) is large-scale by nature. This is not only manifested by the large number of connected devices, but also by the high volumes of traffic that must be accommodated. Cellular networks are indeed a natural candidate for the data tsunami the IoT is expected to generate in conjunction with legacy human-type traffic. However, the random access process for scheduling request represents a major bottleneck to support IoT via LTE cellular networks. Accordingly, this paper develops a mathematical framework to model and study the random access channel (RACH) scalability to accommodate IoT traffic. The developed model is based on stochastic geometry and discrete time Markov chains (DTMC) to account for different access strategies and possible sources of inter-cell and intra-cell interferences. To this end, the developed model is utilized to assess and compare three different access strategies, which incorporate a combination of transmission persistency, back-off, and power ramping. The analysis and the results showcased herewith clearly illustrate the vulnerability of the random access procedure as the IoT intensity grows. Finally, the paper offers insights into effective scenarios for each transmission strategy in terms of IoT intensity and RACH detection thresholds.

Connected through hyperedge HE-1 (induced by source paper "dynamic binary countdown for massive iot random access in dense 5g networks"):
  with members:
    - "enabling lte rach collision multiplicity detection via machine learning"
    - "on the reliability of lte random access performance bounds for machine to machine burst resolution time"
    - "understanding the iot connectivity landscape a contemporary m2m radio technology roadmap"
    - "random access for machine type communication based on bloom filtering"

Connected through hyperedge HE-2 (induced by source paper "spatiotemporal stochastic modeling of iot enabled cellular networks scalability and stability analysis"):
  with members:
    - "properties of an aloha like stability region"
    - "modeling and analysis of cellular networks using stochastic geometry a tutorial"
    - "m2m massive access in lte rach performance evaluation in a smart city scenario"
    - "on the evaluation of lte random access channel overload in a smart city scenario"
    - "analytical modeling of interference aware power control for the uplink of heterogeneous cellular networks"
    - "modeling cellular networks in fading environments with dominant specular components"
    - "the intensity matching approach a tractable stochastic geometry approximation to system level analysis of cellular networks"
    - "are we approaching the fundamental limits of wireless network densification"

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)