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: "sinr model with best server association for high availability studies of wireless networks"
  Abstract: The signal-to-interference-and-noise ratio (SINR) is of key importance for the analysis and design of wireless networks. For addressing new requirements imposed on wireless communication, in particular high availability, a highly accurate modeling of the SINR is needed. We propose a stochastic model of the SINR distribution where shadow fading is characterized by random variables. Therein, the impact of shadow fading on the user association is incorporated by modification of the distributions involved. The SINR model is capable of describing all parts of the SINR distribution in detail, especially the left tail, which is of interest for studies of high availability.

Connected through hyperedge HE-1 (induced by source paper "deep reinforcement learning for unmanned aerial vehicle assisted vehicular networks"):
  with members:
    - "deep reinforcement learning for intelligent transportation systems"
    - "deep reinforcement learning for dynamic multichannel access in wireless networks"
    - "cellular enabled uav communication trajectory optimization under connectivity constraint"
    - "an online ride sharing path planning strategy for public vehicle systems"
    - "power control in uav supported ultra dense networks communications caching and energy transfer"
    - "joint transportation and charging scheduling in public vehicle systems a game theoretic approach"
    - "sgd learns the conjugate kernel class of the network"
    - "capacity of uav enabled multicast channel joint trajectory design and power allocation"

Connected through hyperedge HE-2 (induced by source paper "local large deviation principle large deviation principle and information theory for the signal to interference and noise ratio graph models"):
  with members:
    - "sinr and throughput of dense cellular networks with stretched exponential path loss"
    - "interference modeling in cognitive radio networks a survey"
    - "local large deviations mcmillian theorem for multitype galton watson processes"
    - "lossy asymptotic equipartition property for geometric networked data structures"

Connected through hyperedge HE-3 (induced by source paper "impact of correlated failures in 5g dual connectivity architectures for urllc applications"):
  with members:
    - "how reliable and capable is multi connectivity"
    - "improving network availability of ultra reliable and low latency communications with multi connectivity"
    - "business case and technology analysis for 5g low latency applications"

Connected through hyperedge HE-4 (induced by source paper "improving network availability of ultra reliable and low latency communications with multi connectivity"):
  with members:
    - "joint uplink and downlink resource configuration for ultra reliable and low latency communications"
    - "cross layer optimization for ultra reliable and low latency radio access networks"
    - "ultra reliable short packet communications half duplex or full duplex relaying"
    - "blocklength limited performance of relaying under quasi static rayleigh channels"
    - "finite block length analysis of the incremental redundancy harq"

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)