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: "artificial intelligence as an enabler for cognitive self organizing future networks"
  Abstract: The explosive increase in number of smart devices hosting sophisticated applications is rapidly affecting the landscape of information communication technology industry. Mobile subscriptions, expected to reach 8.9 billion by 2022, would drastically increase the demand of extra capacity with aggregate throughput anticipated to be enhanced by a factor of 1000. In an already crowded radio spectrum, it becomes increasingly difficult to meet ever growing application demands of wireless bandwidth. It has been shown that the allocated spectrum is seldom utilized by the primary users and hence contains spectrum holes that may be exploited by the unlicensed users for their communication. As we enter the Internet Of Things (IoT) era in which appliances of common use will become smart digital devices with rigid performance requirements (such as low latency, energy efficiency, etc.), current networks face the vexing problem of how to create sufficient capacity for such applications. The fifth generation of cellular networks (5G) envisioned to address these challenges are thus required to incorporate cognition and intelligence to resolve the aforementioned issues.

Connected through hyperedge HE-1 (induced by source paper "problem adapted artificial intelligence for online network optimization"):
  with members:
    - "introduction to online convex optimization"
    - "experience driven networking a deep reinforcement learning based approach"
    - "continuous online sequence learning with an unsupervised neural network model"

Connected through hyperedge HE-2 (induced by source paper "adversarial attacks on cognitive self organizing networks the challenge and the way forward"):
  with members:
    - "experience driven networking a deep reinforcement learning based approach"
    - "divide denoise and defend against adversarial attacks"
    - "machine learning for networking workflow advances and opportunities"
    - "deep learning in mobile and wireless networking a survey"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "knowledge defined networking"
    - "shallow and deep networks intrusion detection system a taxonomy and survey"
    - "why and how networks should run themselves"

Connected through hyperedge HE-3 (induced by source paper "unsupervised machine learning for networking techniques applications and research challenges"):
  with members:
    - "federated learning strategies for improving communication efficiency"
    - "deep learning models for wireless signal classification with distributed low cost spectrum sensors"
    - "a survey of machine and deep learning methods for internet of things iot security"
    - "an introduction to deep learning for the physical layer"
    - "shallow and deep networks intrusion detection system a taxonomy and survey"
    - "iot data analytics using deep learning"
    - "from 4g to 5g self organized network management meets machine learning"
    - "why and how networks should run themselves"

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)