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: "cognitive lpwan towards intelligent wireless services in hybrid low power wide area networks"
  Abstract: The relentless development of the Internet of Things (IoT) communication technologies and the gradual maturity of artificial intelligence (AI) have led to a powerful cognitive computing ability. Users can now access efficient and convenient smart services in smart-city, green-IoT, and heterogeneous networks. AI has been applied in various areas, including the intelligent household, advanced health-care, automatic driving, and emotional interactions. This paper focuses on current wireless-communication technologies, including cellular-communication technologies (4G, 5G), low-power wide-area (LPWA) technologies with an unlicensed spectrum (LoRa, SigFox), and other LPWA technologies supported by 3GPP working with an authorized spectrum (EC-GSM, LTE-M, NB-IoT). We put forward a cognitive LPWA-network (Cognitive-LPWAN) architecture to safeguard stable and efficient communications in a heterogeneous IoT. To ensure that the user can employ the AI efficiently and conveniently, we realize a variety of LPWA technologies to safeguard the network layer. In addition, to balance the demand for heterogeneous IoT devices with the communication delay and energy consumption, we put forward the AI-enabled LPWA hybrid method, starting from the perspective of traffic control. The AI algorithm provides the smart control of wireless-communication technology, intelligent applications and services for the choice of different wireless-communication technologies. As an example, we consider the AIWAC emotion interaction system, build the Cognitive-LPWAN and test the proposed AI-enabled LPWA hybrid method. The experimental results show that our scheme can meet the demands of communication-delay applications. Cognitive-LPWAN selects appropriate communication technologies to achieve a better interaction experience.

Connected through hyperedge HE-1 (induced by source paper "cognitive information measurements a new perspective"):
  with members:
    - "label less learning for traffic control in an edge network"
    - "five disruptive technology directions for 5g"

Connected through hyperedge HE-2 (induced by source paper "toward intelligent network optimization in wireless networking an auto learning framework"):
  with members:
    - "machine learning for networking workflow advances and opportunities"
    - "massive mimo and small cells improving energy efficiency by optimal soft cell coordination"
    - "coalitional game theory for communication networks a tutorial"

Connected through hyperedge HE-3 (induced by source paper "distributed learning for low latency machine type communication in a massive internet of things"):
  with members:
    - "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"
    - "what can wireless cellular technologies do about the upcoming smart metering traffic"
    - "reliable reporting for massive m2m communications with periodic resource pooling"
    - "fundamentals of throughput maximization with random arrivals for m2m communications"

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)