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: "deep learning for channel coding via neural mutual information estimation"
  Abstract: End-to-end deep learning for communication systems, i.e., systems whose encoder and decoder are learned, has attracted significant interest recently, due to its performance which comes close to well-developed classical encoder-decoder designs. However, one of the drawbacks of current learning approaches is that a differentiable channel model is needed for the training of the underlying neural networks. In real-world scenarios, such a channel model is hardly available and often the channel density is not even known at all. Some works, therefore, focus on a generative approach, i.e., generating the channel from samples, or rely on reinforcement learning to circumvent this problem. We present a novel approach which utilizes a recently proposed neural estimator of mutual information. We use this estimator to optimize the encoder for a maximized mutual information, only relying on channel samples. Moreover, we show that our approach achieves the same performance as state-of-the-art end-to-end learning with perfect channel model knowledge.

Connected through hyperedge HE-1 (induced by source paper "distortion agnostic deep watermarking"):
  with members:
    - "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"
    - "spectral normalization for generative adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "capacity of continuous channels with memory via directed information neural estimator"):
  with members:
    - "computing the feedback capacity of finite state channels using reinforcement learning"
    - "conditional mutual information neural estimator"
    - "neural entropic estimation a faster path to mutual information estimation"
    - "itene intrinsic transfer entropy neural estimator"
    - "mine mutual information neural estimation"
    - "estimating the directed information and testing for causality"
    - "universal estimation of directed information"
    - "interpretations of directed information in portfolio theory data compression and hypothesis testing"

Connected through hyperedge HE-3 (induced by source paper "conditional mutual information neural estimator"):
  with members:
    - "ccmi classifier based conditional mutual information estimation"
    - "entropy and mutual information in models of deep neural networks"
    - "formal limitations on the measurement of mutual information"
    - "channel agnostic end to end learning based communication systems with conditional gan"
    - "deep learning for the gaussian wiretap channel"
    - "mine mutual information neural estimation"
    - "representation learning with contrastive predictive coding"
    - "an introduction to deep learning for the physical layer"

Connected through hyperedge HE-4 (induced by source paper "joint learning of geometric and probabilistic constellation shaping"):
  with members:
    - "geometric constellation shaping for fiber optic communication systems via end to end learning"
    - "model free training of end to end communication systems"
    - "an introduction to deep learning for the physical layer"
    - "on probabilistic shaping of quadrature amplitude modulation for the nonlinear fiber channel"
    - "constant composition distribution matching"
    - "adam a method for stochastic optimization"
    - "estimating or propagating gradients through stochastic neurons for conditional computation"
    - "on the partition function and random maximum a posteriori perturbations"

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)