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: "analysis of distributed congestion control in cellular vehicle to everything networks"
  Abstract: Cellular Vehicle-to-everything (C-V2X) communication has been proposed in the 3rd Generation Partnership Project release 14 standard to address the latency and reliability requirements of cooperative safety applications. Such applications can involve highly congested vehicular scenarios where the network experiences high data loads. Thus, a sophisticated congestion control solution is vital in order to maintain the network performance required for safety-related applications. With the aid of our high-fidelity link-level network simulator, we investigate the feasibility of implementing the distributed congestion control algorithm specified in SAE J2945/1 standard on top of the C-V2X stack. We describe our implementation and evaluate the performance of transmission rate and range control mechanisms using relevant metrics. Additionally, we identify areas for potential design enhancements and further investigation.

Connected through hyperedge HE-1 (induced by source paper "congestion control mechanisms in ieee 802 11p and sidelink c v2x"):
  with members:
    - "power and packet rate control for vehicular networks in multi application scenarios"
    - "study of the impact of phy and mac parameters in 3gpp c v2v mode 4"

Connected through hyperedge HE-2 (induced by source paper "controlling steering angle for cooperative self driving vehicles utilizing cnn and lstm based deep networks"):
  with members:
    - "self driving car steering angle prediction based on image recognition"
    - "variational end to end navigation and localization"
    - "vision based navigation of autonomous vehicle in roadway environments with unexpected hazards"
    - "multiple access in cellular v2x performance analysis in highly congested vehicular networks"
    - "lstm a search space odyssey"
    - "end to end deep learning for steering autonomous vehicles considering temporal dependencies"
    - "end to end learning for self driving cars"
    - "end to end learning of driving models from large scale video datasets"

Connected through hyperedge HE-3 (induced by source paper "real time hardware in the loop emulation framework for dsrc based connected vehicle applications"):
  with members:
    - "multiple access in cellular v2x performance analysis in highly congested vehicular networks"

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)