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 paradigm for customer experience management in next generation networks challenges and perspectives"
  Abstract: With advancements of next-generation programmable networks a traditional rule-based decision-making may not be able to adapt effectively to changing network and customer requirements and provide optimal customer experience. Customer experience management (CEM) components and implementation challenges with respect to operator, network, and business requirements must be understood to meet required demands. This paper gives an overview of CEM components and their design challenges. We elaborate on data analytics and artificial intelligence driven CEM and their functional differences. This overview provides a path toward autonomous CEM framework in next-generation networks and sets the groundwork for future enhancements.

Connected through hyperedge HE-1 (induced by source paper "the big three a methodology to increase data science roi by answering the questions companies care about"):
  with members:
    - "causal inference for social discrimination reasoning"
    - "challenges of real world reinforcement learning"
    - "deep reinforcement learning for search recommendation and online advertising a survey by xiangyu zhao long xia jiliang tang and dawei yin with martin vesely as coordinator"
    - "causal inference via kernel deviance measures"
    - "why should i trust you explaining the predictions of any classifier"

Connected through hyperedge HE-2 (induced by source paper "traffic prediction and random access control optimization learning and non learning based approaches"):
  with members:
    - "online supervised learning for traffic load prediction in framed aloha networks"
    - "fast adaptive s aloha scheme for event driven machine to machine communications"

Connected through hyperedge HE-3 (induced by source paper "knowledge driven wireless networks with artificial intelligence design challenges and opportunities"):
  with members:
    - "self x design of wireless networks exploiting artificial intelligence and guided learning"

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)