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: "a reinforcement learning approach for efficient opportunistic vehicle to cloud data transfer"
  Abstract: Vehicular crowdsensing is anticipated to become a key catalyst for data-driven optimization in the Intelligent Transportation System (ITS) domain. Yet, the expected growth in massive Machine-type Communication (mMTC) caused by vehicle-to-cloud transmissions will confront the cellular network infrastructure with great capacity-related challenges. A cognitive way for achieving relief without introducing additional physical infrastructure is the application of opportunistic data transfer for delay-tolerant applications. Hereby, the clients schedule their data transmissions in a channel-aware manner in order to avoid retransmissions and interference with other cell users. In this paper, we introduce a novel approach for this type of resourceaware data transfer which brings together supervised learning for network quality prediction with reinforcement learningbased decision making. The performance evaluation is carried out using data-driven network simulation and real world experiments in the public cellular networks of multiple Mobile Network Operators (MNOs) in different scenarios. The proposed transmission scheme significantly outperforms state-of-the-art probabilistic approaches in most scenarios and achieves data rate improvements of up to 181% in uplink and up to 270% in downlink transmission direction in comparison to conventional periodic data transfer.

Connected through hyperedge HE-1 (induced by source paper "limits lightweight machine learning for iot systems with resource limitations"):
  with members:
    - "towards cooperative data rate prediction for future mobile and vehicular 6g networks"
    - "empirical analysis of client based network quality prediction in vehicular multi mno networks"
    - "system of systems modeling analysis and optimization of hybrid vehicular traffic"
    - "boosting vehicle to cloud communication by machine learning enabled context prediction"
    - "toward intelligent vehicular networks a machine learning framework"
    - "fastdeepiot towards understanding and optimizing neural network execution time on mobile and embedded devices"
    - "machine learning based uplink transmission power prediction for lte and upcoming 5g networks using passive downlink indicators"
    - "learning how to communicate in the internet of things finite resources and heterogeneity"

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)