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: "optimizing mission critical data dissemination in massive iot networks"
  Abstract: Mission critical data dissemination in massive Internet of things (IoT) networks imposes constraints on the message transfer delay between devices. Due to low power and communication range of IoT devices, data is foreseen to be relayed over multiple device-to-device (D2D) links before reaching the destination. The coexistence of a massive number of IoT devices poses a challenge in maximizing the successful transmission capacity of the overall network alongside reducing the multi-hop transmission delay in order to support mission critical applications. There is a delicate interplay between the carrier sensing threshold of the contention based medium access protocol and the choice of packet forwarding strategy selected at each hop by the devices. The fundamental problem in optimizing the performance of such networks is to balance the tradeoff between conflicting performance objectives such as the spatial frequency reuse, transmission quality, and packet progress towards the destination. In this paper, we use a stochastic geometry approach to quantify the performance of multi-hop massive IoT networks in terms of the spatial frequency reuse and the transmission quality under different packet forwarding schemes. We also develop a comprehensive performance metric that can be used to optimize the system to achieve the best performance. The results can be used to select the best forwarding scheme and tune the carrier sensing threshold to optimize the performance of the network according to the delay constraints and transmission quality requirements.

Connected through hyperedge HE-1 (induced by source paper "a multi layer feedback system approach to resilient connectivity of remotely deployed mobile internet of things"):
  with members:
    - "placement optimization of uav mounted mobile base stations"
    - "mobile unmanned aerial vehicles uavs for energy efficient internet of things communications"
    - "secure and reconfigurable network design for critical information dissemination in the internet of battlefield things iobt"
    - "association of networked flying platforms with small cells for network centric 5g c ran"
    - "a distributed approach for networked flying platform association with small cells in 5g networks"
    - "backhaul aware robust 3d drone placement in 5g wireless networks"
    - "efficient deployment of multiple unmanned aerial vehicles for optimal wireless coverage"
    - "optimal transport theory for power efficient deployment of unmanned aerial vehicles"

Connected through hyperedge HE-2 (induced by source paper "optimal dynamic contract for spectrum reservation in mission critical unb iot systems"):
  with members:
    - "an enhanced access reservation protocol with a partial preamble transmission mechanism in nb iot systems"
    - "adaptive and resilient revenue maximizing dynamic resource allocation and pricing for cloud enabled iot systems"
    - "2d time frequency interference modelling using stochastic geometry for performance evaluation in low power wide area networks"
    - "spectrum reservation contract design in tv white space networks"

Connected through hyperedge HE-3 (induced by source paper "cognitive connectivity resilience in multi layer remotely deployed mobile internet of things"):
  with members:
    - "secure and reconfigurable network design for critical information dissemination in the internet of battlefield things iobt"
    - "backhaul aware robust 3d drone placement in 5g wireless networks"
    - "optimal transport theory for power efficient deployment of unmanned aerial vehicles"
    - "resilient and decentralized control of multi level cooperative mobile networks to maintain connectivity under adversarial environment"

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)