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: "kernel based adaptive online reconstruction of coverage maps with side information"
  Abstract: In this paper, we address the problem of reconstructing coverage maps from path-loss measurements in cellular networks. We propose and evaluate two kernel-based adaptive online algorithms as an alternative to typical offline methods. The proposed algorithms are application-tailored extensions of powerful iterative methods such as the adaptive projected subgradient method (APSM) and a state-of-the-art adaptive multikernel method. Assuming that the moving trajectories of users are available, it is shown how side information can be incorporated in the algorithms to improve their convergence performance and the quality of the estimation. The complexity is significantly reduced by imposing sparsity awareness in the sense that the algorithms exploit the compressibility of the measurement data to reduce the amount of data that is saved and processed. Finally, we present extensive simulations based on realistic data to show that our algorithms provide fast and robust estimates of coverage maps in real-world scenarios. Envisioned applications include path-loss prediction along trajectories of mobile users as a building block for anticipatory buffering or traffic offloading.

Connected through hyperedge HE-1 (induced by source paper "boosting vehicle to cloud communication by machine learning enabled context prediction"):
  with members:
    - "empirical analysis of client based network quality prediction in vehicular multi mno networks"
    - "machine learning based context predictive car to cloud communication using multi layer connectivity maps for upcoming 5g networks"
    - "machine learning based uplink transmission power prediction for lte and upcoming 5g networks using passive downlink indicators"
    - "discover your competition in lte client based passive data rate prediction by machine learning"
    - "connected vehicular transportation data analytics and traffic dependent networking"
    - "rushing full speed with lte advanced is economical a power consumption analysis"
    - "energy efficient uav communication with trajectory optimization"
    - "b a t mobile leveraging mobility control knowledge for efficient routing in mobile robotic networks"

Connected through hyperedge HE-2 (induced by source paper "empirical analysis of client based network quality prediction in vehicular multi mno networks"):
  with members:
    - "system of systems modeling analysis and optimization of hybrid vehicular traffic"
    - "boosting vehicle to cloud communication by machine learning enabled context prediction"
    - "machine learning based context predictive car to cloud communication using multi layer connectivity maps for upcoming 5g networks"
    - "machine learning based uplink transmission power prediction for lte and upcoming 5g networks using passive downlink indicators"
    - "b a t mobile leveraging mobility control knowledge for efficient routing in mobile robotic networks"
    - "a survey of anticipatory mobile networking context based classification prediction methodologies and optimization techniques"

Connected through hyperedge HE-3 (induced by source paper "machine learning based context predictive car to cloud communication using multi layer connectivity maps for upcoming 5g networks"):
  with members:
    - "machine learning based uplink transmission power prediction for lte and upcoming 5g networks using passive downlink indicators"
    - "discover your competition in lte client based passive data rate prediction by machine learning"
    - "connected vehicular transportation data analytics and traffic dependent networking"

Connected through hyperedge HE-4 (induced by source paper "efficient machine type communication using multi metric context awareness for cars used as mobile sensors in upcoming 5g networks"):
  with members:
    - "discover your competition in lte client based passive data rate prediction by machine learning"
    - "a survey of anticipatory mobile networking context based classification prediction methodologies and optimization techniques"

Connected through hyperedge HE-5 (induced by source paper "the convergence of machine learning and communications"):
  with members:
    - "methods for interpreting and understanding deep neural networks"
    - "deep learning based mimo communications"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "learning in an uncertain world mimo covariance matrix optimization with imperfect feedback"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "context aware computing for the internet of things a survey"
    - "intriguing properties of neural networks"

Connected through hyperedge HE-6 (induced by source paper "energy efficient predictive resource allocation for vod and real time services"):
  with members:
    - "fundamental green tradeoffs progresses challenges and impacts on 5g networks"
    - "an overview of sustainable green 5g networks"
    - "adaptive video streaming for wireless networks with multiple users and helpers"
    - "predictive green wireless access exploiting mobility and application information"

Connected through hyperedge HE-7 (induced by source paper "a survey of anticipatory mobile networking context based classification prediction methodologies and optimization techniques"):
  with members:
    - "context aware scheduling of joint millimeter wave and microwave resources for dual mode base stations"
    - "anticipatory radio resource management for mobile video streaming with linear programming"
    - "on characterization of feasible interference regions in cognitive radio networks"
    - "spatial wireless channel prediction under location uncertainty"
    - "a control theoretic approach to adaptive video streaming in dense wireless networks"
    - "5g cellular key enabling technologies and research challenges"
    - "context aware small cell networks how social metrics improve wireless resource allocation"
    - "anticipatory mobile computing a survey of the state of the art and research challenges"

Connected through hyperedge HE-8 (induced by source paper "anticipatory networking in future generation mobile networks a survey"):
  with members:
    - "spatial wireless channel prediction under location uncertainty"
    - "living on the edge the role of proactive caching in 5g wireless networks"
    - "predictive green wireless access exploiting mobility and application information"
    - "rate adaptation and admission control for video transmission with subjective quality constraints"
    - "predicting a user s next cell with supervised learning based on channel states"
    - "a comparative study of collaborative filtering algorithms"
    - "femtocaching wireless video content delivery through distributed caching helpers"
    - "mobility prediction in wireless ad hoc networks using neural 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)