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: "viwi a deep learning dataset framework for vision aided wireless communications"
  Abstract: The growing role that artificial intelligence and specifically machine learning is playing in shaping the future of wireless communications has opened up many new and intriguing research directions. This paper motivates the research in the novel direction of \textit{vision-aided wireless communications}, which aims at leveraging visual sensory information in tackling wireless communication problems. Like any new research direction driven by machine learning, obtaining a development dataset poses the first and most important challenge to vision-aided wireless communications. This paper addresses this issue by introducing the Vision-Wireless (ViWi) dataset framework. It is developed to be a parametric, systematic, and scalable data generation framework. It utilizes advanced 3D-modeling and ray-tracing softwares to generate high-fidelity synthetic wireless and vision data samples for the same scenes. The result is a framework that does not only offer a way to generate training and testing datasets but helps provide a common ground on which the quality of different machine learning-powered solutions could be assessed.

Connected through hyperedge HE-1 (induced by source paper "deep transfer learning based downlink channel prediction for fdd massive mimo systems"):
  with members:
    - "fdd massive mimo based on efficient downlink channel reconstruction"
    - "deep learning for mmwave beam and blockage prediction using sub 6ghz channels"
    - "decaying momentum helps neural network training"
    - "learning to transfer unsupervised meta domain translation"
    - "deep learning for tdd and fdd massive mimo mapping channels in space and frequency"
    - "model aided deep neural network for source number detection"
    - "deepmimo a generic deep learning dataset for millimeter wave and massive mimo applications"
    - "deep learning for direct hybrid precoding in millimeter wave massive mimo systems"

Connected through hyperedge HE-2 (induced by source paper "millimeter wave base stations with cameras vision aided beam and blockage prediction"):
  with members:
    - "deep learning for mmwave beam and blockage prediction using sub 6ghz channels"
    - "deep learning for tdd and fdd massive mimo mapping channels in space and frequency"
    - "enabling large intelligent surfaces with compressive sensing and deep learning"
    - "deep learning for direct hybrid precoding in millimeter wave massive mimo systems"
    - "deep learning coordinated beamforming for highly mobile millimeter wave systems"
    - "machine learning for reliable mmwave systems blockage prediction and proactive handoff"
    - "an overview of signal processing techniques for millimeter wave mimo systems"
    - "deep residual learning for image recognition"

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.LG(Machine Learning)