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 neural networks based machine learning for wireless networks a tutorial"
  Abstract: Next-generation wireless networks must support ultra-reliable, low-latency communication and intelligently manage a massive number of Internet of Things (IoT) devices in real-time, within a highly dynamic environment. This need for stringent communication quality-of-service (QoS) requirements as well as mobile edge and core intelligence can only be realized by integrating fundamental notions of artificial intelligence (AI) and machine learning across the wireless infrastructure and end-user devices. In this context, this paper provides a comprehensive tutorial that introduces the main concepts of machine learning, in general, and artificial neural networks (ANNs), in particular, and their potential applications in wireless communications. For this purpose, we present a comprehensive overview on a number of key types of neural networks that include feed-forward, recurrent, spiking, and deep neural networks. For each type of neural network, we present the basic architecture and training procedure, as well as the associated challenges and opportunities. Then, we provide an in-depth overview on the variety of wireless communication problems that can be addressed using ANNs, ranging from communication using unmanned aerial vehicles to virtual reality and edge caching.For each individual application, we present the main motivation for using ANNs along with the associated challenges while also providing a detailed example for a use case scenario and outlining future works that can be addressed using ANNs. In a nutshell, this article constitutes one of the first holistic tutorials on the development of machine learning techniques tailored to the needs of future wireless networks.

Connected through hyperedge HE-1 (induced by source paper "fog computing systems state of the art research issues and future trends"):
  with members:
    - "5g network slicing using sdn and nfv a survey of taxonomy architectures and future challenges"
    - "a roadmap toward the resilient internet of things for cyber physical systems"
    - "game theory for multi access edge computing survey use cases and future trends"
    - "a blockchain enabled trustless crowd intelligence ecosystem on mobile edge computing"
    - "machine learning for resource management in cellular and iot networks potentials current solutions and open challenges"
    - "all one needs to know about fog computing and related edge computing paradigms a complete survey"
    - "mitigating jamming attacks using energy harvesting"
    - "game theoretical modelling of network cybersecurity"

Connected through hyperedge HE-2 (induced by source paper "convergence time optimization for federated learning over wireless networks"):
  with members:
    - "machine learning at the wireless edge distributed stochastic gradient descent over the air"
    - "energy efficient federated learning over wireless communication networks"
    - "accelerating dnn training in wireless federated edge learning system"
    - "towards federated learning at scale system design"
    - "in edge ai intelligentizing mobile edge computing caching and communication by federated learning"
    - "a vision of 6g wireless systems applications trends technologies and open research problems"
    - "federated neuromorphic learning of spiking neural networks for low power edge intelligence"
    - "scheduling policies for federated learning in wireless networks"

Connected through hyperedge HE-3 (induced by source paper "multi agent meta reinforcement learning for self powered and sustainable edge computing systems"):
  with members:
    - "a vision of 6g wireless systems applications trends technologies and open research problems"
    - "on the energy and data storage management in energy harvesting wireless communications"
    - "a joint learning and communications framework for federated learning over wireless networks"
    - "wireless network intelligence at the edge"
    - "joint communication computation caching and control in big data multi access edge computing"
    - "online learning for offloading and autoscaling in energy harvesting mobile edge computing"
    - "stochastic joint radio and computational resource management for multi user mobile edge computing systems"
    - "emm energy aware mobility management for mobile edge computing in ultra dense networks"

Connected through hyperedge HE-4 (induced by source paper "intelligent uav base station selection in urban environments a supervised learning approach"):
  with members:
    - "performance analysis of mobile cellular connected drones under practical antenna configurations"
    - "cellular connectivity for uavs network modeling performance analysis and design guidelines"
    - "mobility in the sky performance and mobility analysis for cellular connected uavs"
    - "understanding uav cellular communications from existing networks to massive mimo"
    - "deep learning coordinated beamforming for highly mobile millimeter wave systems"
    - "the sky is not the limit lte for unmanned aerial vehicles"
    - "a tutorial on uavs for wireless networks applications challenges and open problems"
    - "backhaul for low altitude uavs in urban environments"

Connected through hyperedge HE-5 (induced by source paper "risk based optimization of virtual reality over terahertz reconfigurable intelligent surfaces"):
  with members:
    - "on the reliability of wireless virtual reality at terahertz thz frequencies"
    - "a vision of 6g wireless systems applications trends technologies and open research problems"
    - "reconfigurable intelligent surfaces for energy efficiency in wireless communication"
    - "human in the loop wireless communications machine learning and brain aware resource management"
    - "urllc embb slicing to support vr multimodal perceptions over wireless cellular systems"
    - "virtual reality over wireless networks quality of service model and learning based resource management"
    - "rl 2 fast reinforcement learning via slow reinforcement learning"
    - "software defined network controlled switching between millimeter wave and terahertz small cells"

Connected through hyperedge HE-6 (induced by source paper "latency minimized design of secure transmissions in uav aided communications"):
  with members:
    - "energy efficient cooperative secure transmission in multi uav enabled wireless networks"
    - "user association and path planning for uav aided mobile edge computing with energy restriction"
    - "securing uav communications via joint trajectory and power control"
    - "joint optimization of a uav s trajectory and transmit power for covert communications"
    - "joint trajectory and communication design for multi uav enabled wireless networks"
    - "wireless communications with unmanned aerial vehicles opportunities and challenges"

Connected through hyperedge HE-7 (induced by source paper "deep learning for content based personalized viewport prediction of 360 degree vr videos"):
  with members:
    - "a vision of 6g wireless systems applications trends technologies and open research problems"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "virtual reality over wireless networks quality of service model and learning based resource management"

Connected through hyperedge HE-8 (induced by source paper "cellular traffic prediction with recurrent neural network"):
  with members:
    - "cellular traffic prediction and classification a comparative evaluation of lstm and arima"
    - "recent trends in deep learning based natural language processing"

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.IT(Information Theory)