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 free training of end to end communication systems"
  Abstract: The idea of end-to-end learning of communication systems through neural network-based autoencoders has the shortcoming that it requires a differentiable channel model. We present in this paper a novel learning algorithm which alleviates this problem. The algorithm enables training of communication systems with an unknown channel model or with non-differentiable components. It iterates between training of the receiver using the true gradient, and training of the transmitter using an approximation of the gradient. We show that this approach works as well as model-based training for a variety of channels and tasks. Moreover, we demonstrate the algorithm's practical viability through hardware implementation on software-defined radios where it achieves state-of-the-art performance over a coaxial cable and wireless channel.

Connected through hyperedge HE-1 (induced by source paper "distortion agnostic deep watermarking"):
  with members:
    - "deep learning for channel coding via neural mutual information estimation"
    - "romark a robust watermarking system using adversarial training"
    - "steganogan high capacity image steganography with gans"
    - "robust invisible video watermarking with attention"
    - "adversarial training for free"
    - "stegastamp invisible hyperlinks in physical photographs"
    - "redmark framework for residual diffusion watermarking on deep networks"
    - "neural joint source channel coding"

Connected through hyperedge HE-2 (induced by source paper "from learning to meta learning reduced training overhead and complexity for communication systems"):
  with members:
    - "deep transfer learning based downlink channel prediction for fdd massive mimo systems"
    - "meta learning with warped gradient descent"
    - "meta learning without memorization"
    - "modular meta learning with shrinkage"
    - "uncertainty in model agnostic meta learning using variational inference"
    - "on the convergence theory of gradient based model agnostic meta learning algorithms"
    - "on first order meta learning algorithms"
    - "recasting gradient based meta learning as hierarchical bayes"

Connected through hyperedge HE-3 (induced by source paper "wgan based autoencoder training over the air"):
  with members:
    - "concept and experimental demonstration of optical im dd end to end system optimization using a generative model"
    - "trainable communication systems concepts and prototype"
    - "channel agnostic end to end learning based communication systems with conditional gan"
    - "an introduction to deep learning for the physical layer"
    - "learning to communicate channel auto encoders domain specific regularizers and attention"
    - "deep residual learning for image recognition"
    - "conditional generative adversarial nets"

Connected through hyperedge HE-4 (induced by source paper "golay layer limiting peak to average power ratio for ofdm based autoencoders"):
  with members:
    - "one bit ofdm receivers via deep learning"
    - "ofdm autoencoder for end to end learning of communications systems"
    - "a generic complementary sequence encoder"
    - "an introduction to deep learning for the physical layer"
    - "power of deep learning for channel estimation and signal detection in ofdm systems"
    - "suppressing alignment joint papr and out of band power leakage reduction for ofdm based systems"
    - "learning to communicate channel auto encoders domain specific regularizers and attention"

Connected through hyperedge HE-5 (induced by source paper "learning to communicate and energize modulation coding and multiple access designs for wireless information power transmission"):
  with members:
    - "learning modulation design for swipt with nonlinear energy harvester large and small signal power regimes"
    - "signal and system design for wireless power transfer prototype experiment and validation"
    - "fundamentals of wireless information and power transfer from rf energy harvester models to signal and system designs"
    - "deep learning methods for improved decoding of linear codes"
    - "ofdm autoencoder for end to end learning of communications systems"
    - "a learning approach to wireless information and power transfer signal and system design"
    - "an introduction to deep learning for the physical layer"
    - "on the capacity of swipt systems with a nonlinear energy harvesting circuit"

Connected through hyperedge HE-6 (induced by source paper "learning modulation design for swipt with nonlinear energy harvester large and small signal power regimes"):
  with members:
    - "fundamentals of wireless information and power transfer from rf energy harvester models to signal and system designs"
    - "deep learning methods for improved decoding of linear codes"
    - "ofdm autoencoder for end to end learning of communications systems"
    - "a learning approach to wireless information and power transfer signal and system design"
    - "an introduction to deep learning for the physical layer"
    - "on the capacity of swipt systems with a nonlinear energy harvesting circuit"
    - "on capacity achieving distributions over complex awgn channels under nonlinear power constraints and their applications to swipt"
    - "wireless information and power transfer over an awgn channel nonlinearity and asymmetric gaussian signaling"

Connected through hyperedge HE-7 (induced by source paper "design of communication systems using deep learning a variational inference perspective"):
  with members:
    - "learning to detect"
    - "constrained cnn losses for weakly supervised segmentation"
    - "one bit ofdm receivers via deep learning"
    - "end to end deep learning of optical fiber communications"
    - "deep learning in physical layer communications"
    - "channel agnostic end to end learning based communication systems with conditional gan"
    - "an introduction to deep learning for the physical layer"
    - "imposing hard constraints on deep networks promises and limitations"

Connected through hyperedge HE-8 (induced by source paper "trainable communication systems concepts and prototype"):
  with members:
    - "deep learning for the degraded broadcast channel"
    - "joint learning of geometric and probabilistic constellation shaping"
    - "end to end deep learning of optical fiber communications"
    - "online label recovery for deep learning based communication through error correcting codes"
    - "ofdm autoencoder for end to end learning of communications systems"
    - "an introduction to deep learning for the physical layer"
    - "learning to decode linear codes using deep learning"
    - "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.IT(Information Theory)