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: "nonlinear interference mitigation via deep neural networks"
  Abstract: A neural-network-based approach is presented to efficiently implement digital backpropagation (DBP). For a 32x100 km fiber-optic link, the resulting "learned" DBP significantly reduces the complexity compared to conventional DBP implementations.

Connected through hyperedge HE-1 (induced by source paper "what can machine learning teach us about communications"):
  with members:
    - "deep learning methods for improved decoding of linear codes"
    - "wideband time domain digital backpropagation via subband processing and deep learning"
    - "deep learning for decoding of linear codes a syndrome based approach"
    - "asic implementation of time domain digital backpropagation with deep learned chromatic dispersion filters"
    - "learning to decode linear codes using deep learning"
    - "multiple bases belief propagation decoding of high density cyclic codes"

Connected through hyperedge HE-2 (induced by source paper "deep learning for communication over dispersive nonlinear channels performance and comparison with classical digital signal processing"):
  with members:
    - "end to end learning for gmi optimized geometric constellation shape"
    - "end to end optimized transmission over dispersive intensity modulated channels using bidirectional recurrent neural networks"
    - "end to end deep learning of optical fiber communications"
    - "an introduction to deep learning for the physical layer"
    - "applying neural networks in optical communication systems possible pitfalls"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "achievable information rates for nonlinear fiber communication via end to end autoencoder learning"):
  with members:
    - "end to end deep learning of optical fiber communications"
    - "an introduction to deep learning for the physical layer"
    - "on achievable rates for long haul fiber optic communications"
    - "design of apsk constellations for coherent optical channels with nonlinear phase noise"
    - "on the per sample capacity of nondispersive optical fibers"

Connected through hyperedge HE-4 (induced by source paper "end to end deep learning of optical fiber communications"):
  with members:
    - "an introduction to deep learning for the physical layer"
    - "applying neural networks in optical communication systems possible pitfalls"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "a survey on application of machine learning techniques in optical networks"):
  with members:
    - "deep learning based mimo communications"
    - "power of deep learning for channel estimation and signal detection in ofdm systems"

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)