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: "model aided wireless artificial intelligence embedding expert knowledge in deep neural networks towards wireless systems optimization"
  Abstract: Deep learning based on artificial neural networks is a powerful machine learning method that, in the last few years, has been successfully used to realize tasks, e.g., image classification, speech recognition, translation of languages, etc., that are usually simple to execute by human beings but extremely difficult to perform by machines. This is one of the reasons why deep learning is considered to be one of the main enablers to realize the notion of artificial intelligence. In order to identify the best architecture of an artificial neural network that allows one to fit input-output data pairs, the current methodology in deep learning methods consists of employing a data-driven approach. Once the artificial neural network is trained, it is capable of responding to never-observed inputs by providing the optimum output based on past acquired knowledge. In this context, a recent trend in the deep learning community is to complement pure data-driven approaches with prior information based on expert knowledge. In this work, we describe two methods that implement this strategy, which aim at optimizing wireless communication networks. In addition, we illustrate numerical results in order to assess the performance of the proposed approaches compared with pure data-driven implementations.

Connected through hyperedge HE-1 (induced by source paper "power control in cellular massive mimo with varying user activity a deep learning solution"):
  with members:
    - "sum spectral efficiency maximization in massive mimo systems benefits from deep learning"
    - "optimized power control for massive mimo with underlaid d2d communications"
    - "joint transmit and circuit power minimization in massive mimo with downlink sinr constraints when to turn on massive mimo"
    - "large scale fading decoding in cellular massive mimo systems with spatially correlated channels"
    - "learning to optimize training deep neural networks for interference management"
    - "residual dense network for image super resolution"
    - "an introduction to deep learning for the physical layer"
    - "uplink interference reduction in large scale antenna systems"

Connected through hyperedge HE-2 (induced by source paper "a graph neural network approach for scalable wireless power control"):
  with members:
    - "learning to branch accelerating resource allocation in wireless networks"
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "the roadmap to 6g ai empowered wireless networks"
    - "a comprehensive survey on graph neural networks"
    - "learning to optimize training deep neural networks for interference management"
    - "graph convolutional neural networks for web scale recommender systems"
    - "combinatorial optimization with graph convolutional networks and guided tree search"
    - "how powerful are graph neural networks"

Connected through hyperedge HE-3 (induced by source paper "sum spectral efficiency maximization in massive mimo systems benefits from deep learning"):
  with members:
    - "large scale fading decoding in cellular massive mimo systems with spatially correlated channels"
    - "learning to optimize training deep neural networks for interference management"
    - "residual dense network for image super resolution"
    - "optimal pilot and payload power control in single cell massive mimo systems"
    - "densely connected convolutional networks"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "matconvnet convolutional neural networks for matlab"

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.IT(Information Theory)