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 low latency vehicular networks taming the age of information tail"
  Abstract: While the notion of age of information (AoI) has recently emerged as an important concept for analyzing ultra-reliable low-latency communications (URLLC), the majority of the existing works have focused on the average AoI measure. However, an average AoI based design falls short in properly characterizing the performance of URLLC systems as it cannot account for extreme events that occur with very low probabilities. In contrast, in this paper, the main objective is to go beyond the traditional notion of average AoI by characterizing and optimizing a URLLC system while capturing the AoI tail distribution. In particular, the problem of vehicles' power minimization while ensuring stringent latency and reliability constraints in terms of probabilistic AoI is studied. To this end, a novel and efficient mapping between both AoI and queue length distributions is proposed. Subsequently, extreme value theory (EVT) and Lyapunov optimization techniques are adopted to formulate and solve the problem. Simulation results shows a nearly two-fold improvement in terms of shortening the tail of the AoI distribution compared to a baseline whose design is based on the maximum queue length among vehicles, when the number of vehicular user equipment (VUE) pairs is 80. The results also show that this performance gain increases significantly as the number of VUE pairs increases.

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 "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 and low latency vehicular transmission an extreme value theory approach"
    - "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-3 (induced by source paper "a reinforcement learning framework for optimizing age of information in rf powered communication systems"):
  with members:
    - "average peak age of information minimization in uav assisted iot networks"
    - "timely updates in energy harvesting two hop networks offline and online policies"
    - "age optimal trajectory planning for uav assisted data collection"
    - "sending information through status updates"
    - "optimal status update for age of information minimization with an energy harvesting source"
    - "timely updates over an erasure channel"
    - "age and value of information non linear age case"
    - "update or wait how to keep your data fresh"

Connected through hyperedge HE-4 (induced by source paper "deep reinforcement learning for minimizing age of information in uav assisted networks"):
  with members:
    - "age optimal trajectory planning for uav assisted data collection"
    - "optimized path planning for inspection by unmanned aerial vehicles swarm with energy constraints"
    - "machine learning for wireless connectivity and security of cellular connected uavs"
    - "a tutorial on uavs for wireless networks applications challenges and open problems"
    - "throughput maximization for uav enabled wireless powered communication networks"
    - "optimizing age of information in wireless networks with perfect channel state information"
    - "average age of information with hybrid arq under a resource constraint"
    - "update or wait how to keep your data fresh"

Connected through hyperedge HE-5 (induced by source paper "throughput maximization with an average age of information constraint in fading channels"):
  with members:
    - "age optimal power control for status update systems with packet based transmissions"
    - "minimizing the age of information in wireless networks with stochastic arrivals"
    - "not just age but age and quality of information"
    - "average age of information with hybrid arq under a resource constraint"

Connected through hyperedge HE-6 (induced by source paper "taming the tail of maximal information age in wireless industrial networks"):
  with members:
    - "ultra reliable and low latency vehicular transmission an extreme value theory approach"
    - "ultra reliable and low latency wireless communication tail risk and scale"

Connected through hyperedge HE-7 (induced by source paper "reinforcement learning based scheduling algorithm for optimizing age of information in ultra reliable low latency networks"):
  with members:
    - "application of machine learning in wireless networks key techniques and open issues"
    - "age of information and throughput in a shared access network with heterogeneous traffic"

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)