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: "ultra reliable and low latency vehicular transmission an extreme value theory approach"
  Abstract: Considering a Manhattan mobility model in vehicle-to-vehicle networks, this work studies a power minimization problem subject to second-order statistical constraints on latency and reliability, captured by a network-wide maximal data queue length. We invoke results in  extreme value theory  to characterize the statistics of extreme events in terms of the maximal queue length. By leveraging Lyapunov stochastic optimization to deal with network dynamics, we propose two queue-aware power allocation solutions. In contrast with the baseline, our approaches achieve lower mean and variance of the maximal queue length.

Connected through hyperedge HE-1 (induced by source paper "optimized age of information tail for ultra reliable low latency communications in vehicular networks"):
  with members:
    - "a general formula for the stationary distribution of the age of information and its application to single server queues"
    - "optimal control of status updates in a multiple access channel with stability constraints"
    - "average peak age of information minimization in uav assisted iot networks"
    - "saddlepoint approximations for rayleigh block fading channels"
    - "optimal status updating with a finite battery energy harvesting source"
    - "statistical guarantee optimization for aoi in single hop and two hop systems with periodic arrivals"
    - "a vision of 6g wireless systems applications trends technologies and open research problems"
    - "dynamic task offloading and resource allocation for ultra reliable low latency edge computing"

Connected through hyperedge HE-2 (induced by source paper "distributed federated learning for ultra reliable low latency vehicular communications"):
  with members:
    - "a vision of 6g wireless systems applications trends technologies and open research problems"
    - "client selection for federated learning with heterogeneous resources in mobile edge"
    - "beyond 5g with uavs foundations of a 3d wireless cellular network"
    - "federated learning for ultra reliable low latency v2v communications"
    - "federated learning strategies for improving communication efficiency"
    - "joint communication and control for wireless autonomous vehicular platoon systems"
    - "ultra reliable and low latency wireless communication tail risk and scale"
    - "joint status sampling and updating for minimizing age of information in the internet of things"

Connected through hyperedge HE-3 (induced by source paper "intelligent resource slicing for embb and urllc coexistence in 5g and beyond a deep reinforcement learning based approach"):
  with members:
    - "extreme urllc vision challenges and key enablers"
    - "coexistence mechanism between embb and urllc in 5g wireless networks"
    - "embb urllc resource slicing a risk sensitive approach"
    - "ultra reliable and low latency wireless communication tail risk and scale"
    - "coexistence of urllc and embb services in the c ran uplink an information theoretic study"
    - "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"
    - "urllc embb slicing to support vr multimodal perceptions over wireless cellular systems"

Connected through hyperedge HE-4 (induced by source paper "deep reinforcement learning based mode selection and resource allocation for cellular v2x communications"):
  with members:
    - "in edge ai intelligentizing mobile edge computing caching and communication by federated learning"
    - "federated learning strategies for improving communication efficiency"
    - "application of machine learning in wireless networks key techniques and open issues"
    - "deep reinforcement learning based mode selection and resource management for green fog radio access networks"
    - "deep reinforcement learning for resource allocation in v2v communications"
    - "network slicing in fog radio access networks issues and challenges"
    - "communication efficient learning of deep networks from decentralized data"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-5 (induced by source paper "ultra reliable and low latency vehicular communication an active learning approach"):
  with members:
    - "a vision of 6g wireless systems applications trends technologies and open research problems"
    - "ultra reliable low latency vehicular networks taming the age of information tail"
    - "a statistical learning approach to ultra reliable low latency communication"
    - "joint information freshness and completion time optimization for vehicular networks"
    - "fine grained vs average reliability for v2v communications around intersections"
    - "gaussian processes for nonlinear signal processing an overview of recent advances"

Connected through hyperedge HE-6 (induced by source paper "risk sensitive task fetching and offloading for vehicular edge computing"):
  with members:
    - "application of machine learning in wireless networks key techniques and open issues"
    - "ultra reliable and low latency wireless communication tail risk and scale"
    - "a survey on mobile edge computing the communication perspective"
    - "backhaul aware interference management in the uplink of wireless small cell networks"

Connected through hyperedge HE-7 (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"
    - "deep learning for radio resource allocation in multi cell networks"

Connected through hyperedge HE-8 (induced by source paper "dependence control for reliability optimization in vehicular networks"):
  with members:
    - "a vision of 6g wireless systems applications trends technologies and open research problems"
    - "joint communication and control for wireless autonomous vehicular platoon systems"
    - "ultra reliable low latency vehicle to infrastructure wireless communications with edge computing"
    - "applications of stochastic ordering to wireless communications"

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.NI(Networking and Internet Architecture)