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: "deep learning for reliable mobile edge analytics in intelligent transportation systems"
  Abstract: Intelligent transportation systems (ITSs) will be a major component of tomorrow's smart cities. However, realizing the true potential of ITSs requires ultra-low latency and reliable data analytics solutions that can combine, in real-time, a heterogeneous mix of data stemming from the ITS network and its environment. Such data analytics capabilities cannot be provided by conventional cloud-centric data processing techniques whose communication and computing latency can be high. Instead, edge-centric solutions that are tailored to the unique ITS environment must be developed. In this paper, an edge analytics architecture for ITSs is introduced in which data is processed at the vehicle or roadside smart sensor level in order to overcome the ITS latency and reliability challenges. With a higher capability of passengers' mobile devices and intra-vehicle processors, such a distributed edge computing architecture can leverage deep learning techniques for reliable mobile sensing in ITSs. In this context, the ITS mobile edge analytics challenges pertaining to heterogeneous data, autonomous control, vehicular platoon control, and cyber-physical security are investigated. Then, different deep learning solutions for such challenges are proposed. The proposed deep learning solutions will enable ITS edge analytics by endowing the ITS devices with powerful computer vision and signal processing functions. Preliminary results show that the proposed edge analytics architecture, coupled with the power of deep learning algorithms, can provide a reliable, secure, and truly smart transportation environment.

Connected through hyperedge HE-1 (induced by source paper "optimal time allocation in vanets advertising a price based approach using stackelberg game"):
  with members:
    - "dynamic mobility aware interference avoidance for aerial base stations in cognitive radio networks"
    - "robust deep reinforcement learning for security and safety in autonomous vehicle systems"

Connected through hyperedge HE-2 (induced by source paper "sleeping multi armed bandit learning for fast uplink grant allocation in machine type communications"):
  with members:
    - "fast uplink grant for machine type communications challenges and opportunities"
    - "deep learning for signal authentication and security in massive internet of things systems"
    - "random access schemes in wireless systems with correlated user activity"
    - "virtual reality over wireless networks quality of service model and learning based resource management"
    - "deep learning based dynamic watermarking for secure signal authentication in the internet of things"
    - "distributed user association in energy harvesting small cell networks a probabilistic bandit model"
    - "cognitive hierarchy theory for distributed resource allocation in the internet of things"
    - "grant free massive noma outage probability and throughput"

Connected through hyperedge HE-3 (induced by source paper "joint communication computation caching and control in big data multi access edge computing"):
  with members:
    - "proactive edge computing in latency constrained fog networks"
    - "a survey on mobile edge computing the communication perspective"
    - "an online secretary framework for fog network formation with minimal latency"
    - "stochastic joint radio and computational resource management for multi user mobile edge computing systems"
    - "energy efficient resource allocation for cache assisted mobile edge computing"
    - "network slicing for service oriented networks under resource constraints"
    - "artificial neural networks based machine learning for wireless networks a tutorial"
    - "caching in the sky proactive deployment of cache enabled unmanned aerial vehicles for optimized quality of experience"

Connected through hyperedge HE-4 (induced by source paper "deep learning for signal authentication and security in massive internet of things systems"):
  with members:
    - "interference management for cellular connected uavs a deep reinforcement learning approach"
    - "fast uplink grant for machine type communications challenges and opportunities"
    - "deep learning based dynamic watermarking for secure signal authentication in the internet of things"
    - "cognitive hierarchy theory for distributed resource allocation in the internet of things"
    - "artificial neural networks based machine learning for wireless networks a tutorial"
    - "colonel blotto game for secure state estimation in interdependent critical infrastructure"
    - "proactive resource management in lte u systems a deep learning perspective"
    - "deep reinforcement learning from self play in imperfect information games"

Connected through hyperedge HE-5 (induced by source paper "cyber physical security and safety of autonomous connected vehicles optimal control meets multi armed bandit learning"):
  with members:
    - "sleeping multi armed bandit learning for fast uplink grant allocation in machine type communications"
    - "robust deep reinforcement learning for security and safety in autonomous vehicle systems"
    - "joint communication and control for wireless autonomous vehicular platoon systems"
    - "stochastic optimization and control framework for 5g network slicing with effective isolation"
    - "deep learning for signal authentication and security in massive internet of things systems"
    - "a colonel blotto game for interdependence aware cyber physical systems security in smart cities"
    - "a survey on low latency towards 5g ran core network and caching solutions"
    - "provably safe cruise control of vehicular platoons"

Connected through hyperedge HE-6 (induced by source paper "robust deep reinforcement learning for security and safety in autonomous vehicle systems"):
  with members:
    - "joint communication and control for wireless autonomous vehicular platoon systems"
    - "deep learning for signal authentication and security in massive internet of things systems"
    - "artificial neural networks based machine learning for wireless networks a tutorial"
    - "colonel blotto game for secure state estimation in interdependent critical infrastructure"
    - "a colonel blotto game for interdependence aware cyber physical systems security in smart cities"
    - "deep reinforcement learning from self play in imperfect information games"
    - "unmanned aerial vehicle with underlaid device to device communications performance and tradeoffs"
    - "speech recognition with deep recurrent neural networks"

Connected through hyperedge HE-7 (induced by source paper "joint communication and control for wireless autonomous vehicular platoon systems"):
  with members:
    - "modeling and dimensioning of a virtualized mme for 5g mobile networks"
    - "millimeter wave v2v communications distributed association and beam alignment"
    - "coverage analysis of a vehicular network modeled as cox process driven by poisson line process"
    - "surveillance and intervention of infrastructure free mobile communications a new wireless security paradigm"
    - "coverage and rate analysis for millimeter wave cellular networks"

Connected through hyperedge HE-8 (induced by source paper "proactive resource management for lte in unlicensed spectrum a deep learning perspective"):
  with members:
    - "echo state networks for self organizing resource allocation in lte u with uplink downlink decoupling"
    - "lstm a search space odyssey"
    - "inter operator resource management for millimeter wave multi hop backhaul networks"
    - "deep learning based dynamic watermarking for secure signal authentication in the internet of things"
    - "holistic small cell traffic balancing across licensed and unlicensed bands"
    - "coordinated dynamic spectrum management of lte u and wi fi networks"
    - "learning to cooperate via policy search"
    - "long short term memory based recurrent neural network architectures for large vocabulary speech recognition"

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)