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: "stochastic optimization and control framework for 5g network slicing with effective isolation"
  Abstract: Network slicing is an emerging technique for providing resources to diverse wireless services with heterogeneous quality-of-service needs. However, beyond satisfying end-to-end requirements of network users, network slicing needs to also provide isolation between slices so as to prevent one slice's faults and congestion from affecting other slices. In this paper, the problem of network slicing is studied in the context of a wireless system having a time-varying number of users that require two types of slices: reliable low latency (RLL) and self-managed (capacity limited) slices. To address this problem, a novel control framework for stochastic optimization is proposed based on the Lyapunov drift-plus-penalty method. This new framework enables the system to minimize power, maintain slice isolation, and provide reliable and low latency end-to-end communication for RLL slices. Simulation results show that the proposed approach can maintain the system's reliability while providing effective slice isolation in the event of sudden changes in the network environment.

Connected through hyperedge HE-1 (induced by source paper "energy efficient resource allocation in uav enabled mobile edge computing networks"):
  with members:
    - "beyond 5g with uavs foundations of a 3d wireless cellular network"
    - "3 d placement of an unmanned aerial vehicle base station for maximum coverage of users with different qos requirements"
    - "liquid state machine learning for resource and cache management in lte u unmanned aerial vehicle uav networks"
    - "uav aided offloading for cellular hotspot"
    - "joint altitude beamwidth location and bandwidth optimization for uav enabled communications"
    - "joint energy minimization and resource allocation in c ran with mobile cloud"
    - "placement optimization of uav mounted mobile base stations"
    - "joint trajectory and communication design for multi uav enabled wireless networks"

Connected through hyperedge HE-2 (induced by source paper "experienced deep reinforcement learning with generative adversarial networks gans for model free ultra reliable low latency communication"):
  with members:
    - "generative adversarial networks for distributed intrusion detection in the internet of things"
    - "deep learning based end to end wireless communication systems with conditional gan as unknown channel"
    - "a vision of 6g wireless systems applications trends technologies and open research problems"
    - "joint user association and resource allocation optimization for ultra reliable low latency hetnets"
    - "federated learning for ultra reliable low latency v2v communications"
    - "robust deep reinforcement learning for security and safety in autonomous vehicle systems"
    - "risk aware resource allocation for urllc challenges and strategies with machine learning"
    - "ultra reliable and low latency vehicular transmission an extreme value theory approach"

Connected through hyperedge HE-3 (induced by source paper "learning based resource optimization in ultra reliable low latency hetnets"):
  with members:
    - "ultra reliable and low latency wireless communication tail risk and scale"
    - "ultra reliable and low latency communications in 5g downlink physical layer aspects"
    - "load balancing user association in millimeter wave mimo networks"
    - "energy efficient resource allocation for ultra reliable and low latency communications"

Connected through hyperedge HE-4 (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"
    - "deep learning for signal authentication and security in massive internet of things systems"
    - "deep learning for reliable mobile edge analytics in intelligent transportation 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-5 (induced by source paper "risk aware resource allocation for urllc challenges and strategies with machine learning"):
  with members:
    - "self organized low power iot networks a distributed learning approach"
    - "5g wireless network slicing for embb urllc and mmtc a communication theoretic view"
    - "ultra reliable and low latency communications in 5g downlink physical layer aspects"
    - "deep reinforcement learning for resource allocation in v2v communications"
    - "a survey on low latency towards 5g ran core network and caching solutions"

Connected through hyperedge HE-6 (induced by source paper "a survey on low latency towards 5g ran core network and caching solutions"):
  with members:
    - "ultra reliable and low latency communications in 5g downlink physical layer aspects"
    - "human in the loop wireless communications machine learning and brain aware resource management"
    - "r fft function split at ifft fft in unified lte cran and cable access network"
    - "a survey on mobile edge networks convergence of computing caching and communications"
    - "joint user association power control and scheduling in multi cell 5g networks"
    - "fundamental limits on latency in transceiver cache aided hetnets"
    - "decentralized coded caching in wireless networks trade off between storage and latency"
    - "provisioning low latency resilient mobile edge clouds for 5g"

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)