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: "finite blocklength communications in smart grids for dynamic spectrum access and locally licensed scenarios"
  Abstract: This paper focuses on the performance analysis of short blocklength communication with application in smart grids. We use stochastic geometry to compute in closed form the success probability of a typical message transmission as a function of its size (i.e., blocklength), the number of information bits, and the density of interferers. Two different scenarios are investigated: 1) dynamic spectrum access, where the licensed and unlicensed users share the uplink channel frequency band and 2) local licensing approach using the so called micro-operator, which holds an exclusive license of its own. Approximated outage probability expression is derived for the dynamic spectrum access scenario, while a closed-form solution is attained for the micro-operator. The analysis also incorporates the use of retransmissions when messages are detected in error. Our numerical results show how reliability and delay are related in either scenarios.

Connected through hyperedge HE-1 (induced by source paper "hybrid resource scheduling for aggregation in massive machine type communication networks"):
  with members:
    - "massive multiple access based on superposition raptor codes for cellular m2m communications"
    - "massive non orthogonal multiple access for cellular iot potentials and limitations"
    - "aggregation and resource scheduling in machine type communication networks a stochastic geometry approach"
    - "spatiotemporal stochastic modeling of iot enabled cellular networks scalability and stability analysis"
    - "massive machine type communication with data aggregation and resource scheduling"
    - "maximizing the link throughput between smart meters and aggregators as secondary users under power and outage constraints"
    - "asymptotics and approximation of the sir distribution in general cellular networks"
    - "optimizing data aggregation for uplink machine to machine communication networks"

Connected through hyperedge HE-2 (induced by source paper "a throughput and energy efficiency scheme for unlicensed massive machine type communications"):
  with members:
    - "5g wireless network slicing for embb urllc and mmtc a communication theoretic view"
    - "energy efficiency of an unlicensed wireless network in the presence of retransmissions"
    - "increasing the throughput of an unlicensed wireless network through retransmissions"
    - "massive machine type communications in 5g physical and mac layer solutions"
    - "next generation m2m cellular networks challenges and practical considerations"
    - "a survey on internet of things from industrial market perspective"
    - "on the joint impact of beamwidth and orientation error on throughput in directional wireless poisson networks"
    - "green cellular networks a survey some research issues and challenges"

Connected through hyperedge HE-3 (induced by source paper "energy internet via packetized management enabling technologies and deployment challenges"):
  with members:
    - "event based electricity metering an autonomous method to determine transmission thresholds"
    - "sparse signal processing for grant free massive connectivity a future paradigm for random access protocols in the internet of things"
    - "why smart appliances may result in a stupid energy grid"
    - "increasing the throughput of an unlicensed wireless network through retransmissions"
    - "narrowband iot data transmission procedures for massive machine type communications"
    - "multi layer analysis of iot based systems"
    - "aggregation and resource scheduling in machine type communication networks a stochastic geometry approach"
    - "self regulating supply demand systems"

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)