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: "long term mobile traffic forecasting using deep spatio temporal neural networks"
  Abstract: Forecasting with high accuracy the volume of data traffic that mobile users will consume is becoming increasingly important for precision traffic engineering, demand-aware network resource allocation, as well as public transportation. Measurements collection in dense urban deployments is however complex and expensive, and the post-processing required to make predictions is highly non-trivial, given the intricate spatio-temporal variability of mobile traffic due to user mobility. To overcome these challenges, in this paper we harness the exceptional feature extraction abilities of deep learning and propose a Spatio-Temporal neural Network (STN) architecture purposely designed for precise network-wide mobile traffic forecasting. We present a mechanism that fine tunes the STN and enables its operation with only limited ground truth observations. We then introduce a Double STN technique (D-STN), which uniquely combines the STN predictions with historical statistics, thereby making faithful long-term mobile traffic projections. Experiments we conduct with real-world mobile traffic data sets, collected over 60 days in both urban and rural areas, demonstrate that the proposed (D-)STN schemes perform up to 10-hour long predictions with remarkable accuracy, irrespective of the time of day when they are triggered. Specifically, our solutions achieve up to 61% smaller prediction errors as compared to widely used forecasting approaches, while operating with up to 600 times shorter measurement intervals.

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 "deep learning at the edge"):
  with members:
    - "faster gaze prediction with dense networks and fisher pruning"
    - "quantizing deep convolutional networks for efficient inference a whitepaper"
    - "pact parameterized clipping activation for quantized neural networks"
    - "deep learning in mobile and wireless networking a survey"
    - "deep learning for iot big data and streaming analytics a survey"
    - "wrpn wide reduced precision networks"
    - "hashnet deep learning to hash by continuation"
    - "distributed deep neural networks over the cloud the edge and end devices"

Connected through hyperedge HE-3 (induced by source paper "forecasting trajectory and behavior of road agents using spectral clustering in graph lstms"):
  with members:
    - "grip graph based interaction aware trajectory prediction"
    - "robusttp end to end trajectory prediction for heterogeneous road agents in dense traffic with noisy sensor inputs"
    - "sr lstm state refinement for lstm towards pedestrian trajectory prediction"
    - "self driving cars a survey"
    - "multi agent tensor fusion for contextual trajectory prediction"
    - "predicting motion of vulnerable road users using high definition maps and efficient convnets"
    - "a behavior driven approach for sampling rare event situations for autonomous vehicles"
    - "nuscenes a multimodal dataset for autonomous driving"

Connected through hyperedge HE-4 (induced by source paper "cloudlstm a recurrent neural model for spatiotemporal point cloud stream forecasting"):
  with members:
    - "multi service mobile traffic forecasting via convolutional long short term memories"
    - "predrnn towards a resolution of the deep in time dilemma in spatiotemporal predictive learning"
    - "machine learning for spatiotemporal sequence forecasting a survey"
    - "deep learning in mobile and wireless networking a survey"
    - "voxelnet end to end learning for point cloud based 3d object detection"
    - "deep learning for precipitation nowcasting a benchmark and a new model"
    - "zipnet gan inferring fine grained mobile traffic patterns via a generative adversarial neural network"
    - "deformable convolutional networks"

Connected through hyperedge HE-5 (induced by source paper "max min fair resource allocation in millimetre wave backhauls"):
  with members:
    - "adaptive beamwidth selection for contention based access periods in millimeter wave wlans"
    - "inter operator resource management for millimeter wave multi hop backhaul networks"
    - "maximising the utility of enterprise millimetre wave networks"
    - "position aided beam alignment for millimeter wave backhaul systems with large phased arrays"
    - "dynamic time domain duplexing for self backhauled millimeter wave cellular networks"
    - "channel estimation and hybrid precoding for millimeter wave cellular systems"
    - "spatially sparse precoding in millimeter wave mimo systems"
    - "millimeter wave beamforming for wireless backhaul and access in small cell networks"

Connected through hyperedge HE-6 (induced by source paper "multi service mobile traffic forecasting via convolutional long short term memories"):
  with members:
    - "deep learning in mobile and wireless networking a survey"
    - "tensorlayer a versatile library for efficient deep learning development"
    - "tensorflow a system for large scale machine learning"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "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-7 (induced by source paper "high order graph convolutional recurrent neural network a deep learning framework for network scale traffic learning and forecasting"):
  with members:
    - "two stream multi channel convolutional neural network tm cnn for multi lane traffic speed prediction considering traffic volume impact"
    - "mixhop higher order graph convolutional architectures via sparsified neighborhood mixing"
    - "3d graph convolutional networks with temporal graphs a spatial information free framework for traffic forecasting"
    - "deep bidirectional and unidirectional lstm recurrent neural network for network wide traffic speed prediction"
    - "forecasting transportation network speed using deep capsule networks with nested lstm models"
    - "spatio temporal graph convolutional networks a deep learning framework for traffic forecasting"
    - "diffusion convolutional recurrent neural network data driven traffic forecasting"
    - "spatiotemporal recurrent convolutional networks for traffic prediction in transportation 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)