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 based cognitive routing for cognitive radio networks"
  Abstract: Cognitive radio networks (CRNs) are networks of nodes equipped with cognitive radios that can optimize performance by adapting to network conditions. Although various routing protocols incorporating varying degrees of adaptiveness and cognition have been proposed for CRNs, these works have mostly been limited by their system-level focus (that emphasizes optimization at the level of an individual cognitive radio system). The vision of CRNs as cognitive networks, however, requires that the research focus progresses from its current system-level fixation to the a network-wide optimization focus. This motivates the development of cognitive routing protocols envisioned as routing protocols that fully and seamlessly incorporate artificial intelligence (AI)-based techniques into their design. In this paper, we provide a self-contained exposition of various decision-theoretic and learning techniques from the field of AI and machine-learning that are relevant to the problem of cognitive routing in CRNs. Apart from providing necessary background, we present for each technique discussed in this paper their application in the context of CRNs in general and for the routing problem in particular. We also highlight challenges associated with these techniques and common pitfalls. Finally, open research issues and future directions of work are identified.

Connected through hyperedge HE-1 (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"

Connected through hyperedge HE-2 (induced by source paper "qos in ieee 802 11 based wireless networks a contemporary review"):
  with members:
    - "what will 5g be"
    - "building programmable wireless networks an architectural survey"
    - "resource allocation in a network based cloud computing environment design challenges"

Connected through hyperedge HE-3 (induced by source paper "genetic algorithms in wireless networking techniques applications and issues"):
  with members:
    - "qos in ieee 802 11 based wireless networks a contemporary review"
    - "energy efficiency in decentralized wireless networks a game theoretic approach inspired by evolutionary biology"
    - "machine learning in wireless sensor networks algorithms strategies and applications"
    - "building programmable wireless networks an architectural survey"
    - "evolutionary algorithms for reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "multicasting in cognitive radio networks"):
  with members:
    - "minimum cost multicast over coded packet networks"

Connected through hyperedge HE-5 (induced by source paper "building programmable wireless networks an architectural survey"):
  with members:
    - "research directions in network service chaining"

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)