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: "radio transformer networks attention models for learning to synchronize in wireless systems"
  Abstract: We introduce learned attention models into the radio machine learning domain for the task of modulation recognition by leveraging spatial transformer networks and introducing new radio domain appropriate transformations. This attention model allows the network to learn a localization network capable of synchronizing and normalizing a radio signal blindly with zero knowledge of the signals structure based on optimization of the network for classification accuracy, sparse representation, and regularization. Using this architecture we are able to outperform our prior results in accuracy vs signal to noise ratio against an identical system without attention, however we believe such an attention model has implication far beyond the task of modulation recognition.

Connected through hyperedge HE-1 (induced by source paper "unsupervised linear and nonlinear channel equalization and decoding using variational autoencoders"):
  with members:
    - "on the convergence of adam and beyond"
    - "learning from the syndrome"
    - "deep learning methods for improved decoding of linear codes"
    - "channel agnostic end to end learning based communication systems with conditional gan"
    - "an introduction to deep learning for the physical layer"
    - "deep complex networks"
    - "scaling deep learning based decoding of polar codes via partitioning"
    - "on deep learning based channel decoding"

Connected through hyperedge HE-2 (induced by source paper "statistical transformer networks learning shape and appearance models via self supervision"):
  with members:
    - "unsupervised learning of object frames by dense equivariant image labelling"
    - "convolutional neural network architecture for geometric matching"
    - "deformable convolutional networks"
    - "3d morphable models as spatial transformer networks"
    - "mofa model based deep convolutional face autoencoder for unsupervised monocular reconstruction"
    - "toward end to end face recognition through alignment learning"
    - "dense transformer networks"
    - "faceposenet making a case for landmark free face alignment"

Connected through hyperedge HE-3 (induced by source paper "ofdm autoencoder for end to end learning of communications systems"):
  with members:
    - "an introduction to deep learning for the physical layer"
    - "applying neural networks in optical communication systems possible pitfalls"
    - "power of deep learning for channel estimation and signal detection in ofdm systems"

Connected through hyperedge HE-4 (induced by source paper "deep architectures for modulation recognition"):
  with members:
    - "convolutional radio modulation recognition networks"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "going deeper with convolutions"
    - "understanding deep architectures using a recursive convolutional network"
    - "do deep nets really need to be deep"

Connected through hyperedge HE-5 (induced by source paper "deep neural network architectures for modulation classification"):
  with members:
    - "deep architectures for modulation recognition"
    - "densely connected convolutional networks"
    - "convolutional radio modulation recognition networks"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "end to end radio traffic sequence recognition with deep recurrent neural networks"):
  with members:
    - "seqgan sequence generative adversarial nets with policy gradient"
    - "convolutional radio modulation recognition networks"
    - "wavenet a generative model for raw audio"
    - "learning to communicate channel auto encoders domain specific regularizers and attention"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "learning sequence neighbourhood metrics"

Connected through hyperedge HE-7 (induced by source paper "learning to communicate channel auto encoders domain specific regularizers and attention"):
  with members:
    - "unsupervised representation learning of structured radio communication signals"
    - "spatial transformer networks"
    - "equilibrated adaptive learning rates for non convex optimization"
    - "adam a method for stochastic optimization"

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)