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: "network slicing based 5g and future mobile networks mobility resource management and challenges"
  Abstract: The fifth-generation (5G) networks are expected to be able to satisfy users' different quality-of-service (QoS) requirements. Network slicing is a promising technology for 5G networks to provide services tailored for users' specific QoS demands. Driven by the increased massive wireless data traffic from different application scenarios, efficient resource allocation schemes should be exploited to improve the flexibility of network resource allocation and capacity of 5G networks based on network slicing. Due to the diversity of 5G application scenarios, new mobility management schemes are greatly needed to guarantee seamless handover in network slicing based 5G systems. In this article, we introduce a logical architecture for network slicing based 5G systems, and present a scheme for managing mobility between different access networks, as well as a joint power and subchannel allocation scheme in spectrum-sharing two-tier systems based on network slicing, where both the co-tier interference and cross-tier interference are taken into account. Simulation results demonstrate that the proposed resource allocation scheme can flexibly allocate network resources between different slices in 5G systems. Finally, several open issues and challenges in network slicing based 5G networks are discussed, including network reconstruction, network slicing management and cooperation with other 5G technologies.

Connected through hyperedge HE-1 (induced by source paper "a survey on machine learning based performance improvement of wireless networks phy mac and network layer"):
  with members:
    - "learning to optimize training deep neural networks for interference management"
    - "realtime scheduling and power allocation using deep neural networks"
    - "over the air deep learning based radio signal classification"
    - "active learning for wireless iot intrusion detection"
    - "electrosense open and big spectrum data"
    - "deep reinforcement learning for dynamic multichannel access in wireless networks"
    - "deep learning models for wireless signal classification with distributed low cost spectrum sensors"
    - "adversarial attacks on deep learning based radio signal classification"

Connected through hyperedge HE-2 (induced by source paper "5g network slicing using sdn and nfv a survey of taxonomy architectures and future challenges"):
  with members:
    - "a novel qoe aware sdn enabled nfv based management architecture for future multimedia applications on 5g systems"
    - "securing fog to things environment using intrusion detection system based on ensemble learning"
    - "wireless network slicing generalized kelly mechanism based resource allocation"
    - "resource orchestration of 5g transport networks for vertical industries"
    - "slice as an evolutionary service genetic optimization for inter slice resource management in 5g networks"
    - "network slicing for 5g with sdn nfv concepts architectures and challenges"
    - "a survey on mobile edge computing the communication perspective"
    - "nfv and sdn key technology enablers for 5g networks"

Connected through hyperedge HE-3 (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-4 (induced by source paper "distributed resource allocation for network slicing of bandwidth and computational resource"):
  with members:
    - "distributed resource allocation for network slicing over licensed and unlicensed bands"
    - "joint optimization of computation and communication power in multi user massive mimo systems"
    - "computing resource allocation in three tier iot fog networks a joint optimization approach combining stackelberg game and matching"
    - "heterogeneous cellular networks with spatio temporal traffic delay analysis and scheduling"
    - "spatial spectrum and energy efficiency of random cellular networks"
    - "5g ultra dense cellular networks"

Connected through hyperedge HE-5 (induced by source paper "a survey on the security and the evolution of osmotic and catalytic computing for 5g networks"):
  with members:
    - "5g nr jamming spoofing and sniffing threat assessment and mitigation"
    - "a framework for mitigating zero day attacks in iot"
    - "nhad neuro fuzzy based horizontal anomaly detection in online social networks"
    - "multi radio 5g architecture for connected and autonomous vehicles application and design insights"
    - "rehand secure region based fast handover with user anonymity for small cell networks in 5g"
    - "robust physical layer security for power domain non orthogonal multiple access based hetnets and hudns sic avoidance at eavesdroppers"
    - "a survey on the security of pervasive online social networks posns"
    - "a survey of physical layer security techniques for 5g wireless networks and challenges ahead"

Connected through hyperedge HE-6 (induced by source paper "gan based deep distributional reinforcement learning for resource management in network slicing"):
  with members:
    - "deep reinforcement learning with discrete normalized advantage functions for resource management in network slicing"
    - "low complexity distributed radio access network slicing algorithms and experimental results"
    - "intelligent power control for spectrum sharing in cognitive radios a deep reinforcement learning approach"
    - "slice as an evolutionary service genetic optimization for inter slice resource management in 5g networks"
    - "implicit quantile networks for distributional reinforcement learning"
    - "distributed distributional deterministic policy gradients"
    - "network slicing for 5g with sdn nfv concepts architectures and challenges"
    - "a distributional perspective on reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "cloud based queuing model for tactile internet in next generation of ran"):
  with members:
    - "resource management and admission control for tactile internet in next generation of ran"
    - "joint uplink and downlink resource configuration for ultra reliable and low latency communications"
    - "power domain non orthogonal multiple access noma in 5g systems potentials and challenges"
    - "realizing the tactile internet haptic communications over next generation 5g cellular networks"
    - "joint millimeter wave fronthaul and ofdma resource allocation in ultra dense cran"
    - "uplink transmission design with massive machine type devices in tactile internet"
    - "fronthauling for 5g lte u ultra dense cloud small cell networks"
    - "resource management in non orthogonal multiple access networks for 5g and beyond"

Connected through hyperedge HE-8 (induced by source paper "cyber physical systems resilience state of the art research issues and future trends"):
  with members:
    - "a roadmap toward the resilient internet of things for cyber physical systems"
    - "a blockchain enabled trustless crowd intelligence ecosystem on mobile edge computing"
    - "mitigating jamming attacks using energy harvesting"
    - "fundamental concepts of cyber resilience introduction and overview"
    - "tarco two stage auction for d2d relay aided computation resource allocation in hetnet"
    - "an introduction to deep learning for the physical layer"
    - "sustainable incentives for mobile crowdsensing"
    - "towards a cognitive routing engine for software defined networks"

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)