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: "understanding mobile traffic patterns of large scale cellular towers in urban environment"
  Abstract: Understanding mobile traffic patterns of large scale cellular towers in urban environment is extremely valuable for Internet service providers, mobile users, and government managers of modern metropolis. This paper aims at extracting and modeling the traffic patterns of large scale towers deployed in a metropolitan city. To achieve this goal, we need to address several challenges, including lack of appropriate tools for processing large scale traffic measurement data, unknown traffic patterns, as well as handling complicated factors of urban ecology and human behaviors that affect traffic patterns. Our core contribution is a powerful model which combines three dimensional information (time, locations of towers, and traffic frequency spectrum) to extract and model the traffic patterns of thousands of cellular towers. Our empirical analysis reveals the following important observations. First, only five basic time-domain traffic patterns exist among the 9,600 cellular towers. Second, each of the extracted traffic pattern maps to one type of geographical locations related to urban ecology, including residential area, business district, transport, entertainment, and comprehensive area. Third, our frequency domain traffic spectrum analysis suggests that the traffic of any tower among the 9,600 can be constructed using a linear combination of four primary components corresponding to human activity behaviors. We believe that the proposed traffic patterns extraction and modeling methodology, combined with the empirical analysis on the mobile traffic, pave the way toward a deep understanding of the traffic patterns of large scale cellular towers in modern metropolis.

Connected through hyperedge HE-1 (induced by source paper "a survey on machine learning based performance improvement of wireless networks phy mac and network layer"):
  with members:
    - "learning to optimize training deep neural networks for interference management"
    - "realtime scheduling and power allocation using deep neural networks"
    - "over the air deep learning based radio signal classification"
    - "active learning for wireless iot intrusion detection"
    - "electrosense open and big spectrum data"
    - "deep reinforcement learning for dynamic multichannel access in wireless networks"
    - "deep learning models for wireless signal classification with distributed low cost spectrum sensors"
    - "adversarial attacks on deep learning based radio signal classification"

Connected through hyperedge HE-2 (induced by source paper "iris deep reinforcement learning driven shared spectrum access architecture for indoor neutral host small cells"):
  with members:
    - "deepmind control suite"
    - "on spectrum sharing among micro operators in 5g"
    - "shared spectrum access communications a neutral host micro operator approach"
    - "multi agent actor critic for mixed cooperative competitive environments"
    - "deep reinforcement learning for robotic manipulation with asynchronous off policy updates"
    - "resource allocation in shared spectrum access communications for operators with diverse service requirements"
    - "benchmarking deep reinforcement learning for continuous control"
    - "high dimensional continuous control using generalized advantage estimation"

Connected through hyperedge HE-3 (induced by source paper "differentially private aggregated mobility data publication using moving characteristics"):
  with members:
    - "trajectory recovery from ash user privacy is not preserved in aggregated mobility data"
    - "calibrating data to sensitivity in private data analysis"

Connected through hyperedge HE-4 (induced by source paper "cellular network traces towards 5g usage analysis and generation"):
  with members:
    - "advanced coordinated beamforming for the downlink of future lte cellular networks"
    - "a sharing and competition aware framework for cellular network evolution planning"

Connected through hyperedge HE-5 (induced by source paper "zipnet gan inferring fine grained mobile traffic patterns via a generative adversarial neural network"):
  with members:
    - "photo realistic single image super resolution using a generative adversarial network"
    - "densely connected convolutional networks"
    - "residual networks behave like ensembles of relatively shallow networks"
    - "visualizing and understanding neural models in nlp"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "learning deconvolution network for semantic segmentation"

Connected through hyperedge HE-6 (induced by source paper "understanding performance of edge content caching for mobile video streaming"):
  with members:
    - "placing dynamic content in caches with small population"
    - "content caching and delivery over heterogeneous wireless networks"
    - "forecasting popularity of videos using social media"
    - "efficient analysis of caching strategies under dynamic content popularity"
    - "fake view analytics in online video services"
    - "exploring the memory bandwidth tradeoff in an information centric network"
    - "asymptotic laws for joint content replication and delivery in wireless networks"
    - "fundamental limits of caching"

Connected through hyperedge HE-7 (induced by source paper "long term mobile traffic forecasting using deep spatio temporal neural networks"):
  with members:
    - "zipnet gan inferring fine grained mobile traffic patterns via a generative adversarial neural network"
    - "tensorlayer a versatile library for efficient deep learning development"
    - "residual networks behave like ensembles of relatively shallow networks"
    - "recurrent neural networks for multivariate time series with missing values"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "convolutional lstm network a machine learning approach for precipitation nowcasting"
    - "adam a method for stochastic optimization"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-8 (induced by source paper "trajectory recovery from ash user privacy is not preserved in aggregated mobility data"):
  with members:
    - "data for development the d4d challenge on mobile phone data"

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)