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: "achievable information rates for nonlinear fiber communication via end to end autoencoder learning"
  Abstract: Machine learning is used to compute achievable information rates (AIRs) for a simplified fiber channel. The approach jointly optimizes the input distribution (constellation shaping) and the auxiliary channel distribution to compute AIRs without explicit channel knowledge in an end-to-end fashion.

Connected through hyperedge HE-1 (induced by source paper "end to end optimized transmission over dispersive intensity modulated channels using bidirectional recurrent neural networks"):
  with members:
    - "end to end deep learning of optical fiber communications"
    - "an introduction to deep learning for the physical layer"
    - "recurrent additive networks"
    - "applying neural networks in optical communication systems possible pitfalls"
    - "adam a method for stochastic optimization"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

Connected through hyperedge HE-2 (induced by source paper "model free training of end to end communication systems"):
  with members:
    - "one bit ofdm receivers via deep learning"
    - "deep joint source channel coding for wireless image transmission"
    - "neural joint source channel coding"
    - "ofdm autoencoder for end to end learning of communications systems"
    - "channel agnostic end to end learning based communication systems with conditional gan"
    - "end to end learning of communications systems without a channel model"
    - "approximating the void learning stochastic channel models from observation with variational generative adversarial networks"
    - "a learning approach to wireless information and power transfer signal and system design"

Connected through hyperedge HE-3 (induced by source paper "deep learning for the gaussian wiretap channel"):
  with members:
    - "end to end deep learning of optical fiber communications"
    - "ofdm autoencoder for end to end learning of communications systems"
    - "a survey of physical layer security techniques for 5g wireless networks and challenges ahead"
    - "end to end learning of communications systems without a channel model"
    - "an introduction to deep learning for the physical layer"
    - "learning to protect communications with adversarial neural cryptography"
    - "infogan interpretable representation learning by information maximizing generative adversarial nets"
    - "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)