human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "when machine learning meets wireless cellular networks deployment challenges and applications"):
  Abstract: Artificial intelligence (AI) powered wireless networks promise to revolutionize the conventional operation and structure of current networks from network design to infrastructure management, cost reduction, and user performance improvement. Empowering future networks with AI functionalities will enable a shift from reactive/incident driven operations to proactive/data driven operations. This paper provides an overview on the integration of AI functionalities in 5G and beyond networks. Key factors for successful AI integration such as data, real-time network intelligence, security, and augmentation of human intelligence are highlighted. We also summarize the various types of network intelligence as well as machine learning based air interface in future networks. Use case examples for the application of AI to the wireless domain are then summarized. We highlight on applications to the physical layer, radio resource management, mobility management, wireless security, and localization.

Includes members:
  Member "deepcabac context adaptive binary arithmetic coding for deep neural network compression":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "scalable model compression by entropy penalized reparameterization")
  Member "human centered artificial intelligence and machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "attention is not not explanation")
  Member "machine learning in the air":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deep learning for massive mimo channel state acquisition and feedback")
  Member "ofdm autoencoder for end to end learning of communications systems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "golay layer limiting peak to average power ratio for ofdm based autoencoders")
  Member "scalable agent alignment via reward modeling a research direction":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "ai safety state of the field through quantitative lens")
  Member "rogue drone detection a machine learning approach":
    also connected through other hyperedges:
      (none)
  Member "explainable artificial intelligence understanding visualizing and interpreting deep learning models":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on the convergence of artificial intelligence and distributed ledger technology a scoping review and future research agenda")
  Member "artificial neural networks based machine learning for wireless networks a tutorial":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "fog computing systems state of the art research issues and future trends")

Question: Please tell me which class the source paper 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.IT(Information Theory)