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 communication an active learning approach"
  Abstract: In this letter, an age of information (AoI)-aware transmission power and resource block (RB) allocation technique for vehicular communication networks is proposed. Due to the highly dynamic nature of vehicular networks, gaining a prior knowledge about the network dynamics, i.e., wireless channels and interference, in order to allocate resources, is challenging. Therefore, to effectively allocate power and RBs, the proposed approach allows the network to actively learn its dynamics by balancing a tradeoff between minimizing the probability that the vehicles ’ AoI exceeds a predefined threshold and maximizing the knowledge about the network dynamics. In this regard, using a Gaussian process regression (GPR) approach, an online decentralized strategy is proposed to actively learn the network dynamics, estimate the vehicles ’ future AoI, and proactively allocate resources. Simulation results show a significant improvement in terms of AoI violation probability, compared to several baselines, with a reduction of at least 50%.

Connected through hyperedge HE-1 (induced by source paper "deep learning assisted csi estimation for joint urllc and embb resource allocation"):
  with members:
    - "embb urllc resource slicing a risk sensitive approach"
    - "wireless network intelligence at the edge"
    - "exploiting wireless channel state information structures beyond linear correlations a deep learning approach"
    - "federated learning for ultra reliable low latency v2v communications"
    - "channel charting locating users within the radio environment using channel state information"
    - "channel acquisition for massive mimo ofdm with adjustable phase shift pilots"
    - "adam a method for stochastic optimization"
    - "distributed compressive csit estimation and feedback for fdd multi user massive mimo systems"

Connected through hyperedge HE-2 (induced by source paper "extreme urllc vision challenges and key enablers"):
  with members:
    - "massive autonomous uav path planning a neural network based mean field game theoretic approach"
    - "one pixel image and rf signal based split learning for mmwave received power prediction"
    - "time synchronization in 5g wireless edge requirements and solutions for critical mtc"
    - "a vision of 6g wireless systems applications trends technologies and open research problems"
    - "wireless network intelligence at the edge"
    - "urllc embb slicing to support vr multimodal perceptions over wireless cellular systems"
    - "a statistical learning approach to ultra reliable low latency communication"

Connected through hyperedge HE-3 (induced by source paper "active learning based classification in automated connected vehicles"):
  with members:
    - "active self paced learning for cost effective and progressive face identification"
    - "cost effective active learning for deep image classification"
    - "learning with symmetric label noise the importance of being unhinged"

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)