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: "applying neural networks in optical communication systems possible pitfalls"
  Abstract: We investigate the risk of overestimating the performance gain when applying neural network-based receivers in systems with pseudorandom bit sequences or with limited memory depths, resulting in repeated short patterns. We show that with such sequences, a large artificial gain can be obtained, which comes from pattern prediction rather than predicting or compensating the studied channel/phenomena.

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"
    - "achievable information rates for nonlinear fiber communication via end to end autoencoder learning"
    - "an introduction to deep learning for the physical layer"
    - "recurrent additive networks"
    - "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 "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"
    - "nonlinear interference mitigation via deep neural networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "geometric constellation shaping for fiber optic communication systems via end to end learning"):
  with members:
    - "experimental demonstration of geometrically shaped constellations tailored to the nonlinear fibre channel"
    - "a survey on application of machine learning techniques in optical networks"
    - "deep learning of the nonlinear schr odinger equation in fiber optic communications"
    - "end to end learning of communications systems without a channel model"
    - "deep learning of geometric constellation shaping including fiber nonlinearities"
    - "an introduction to deep learning for the physical layer"
    - "impact of 4d channel distribution on the achievable rates in coherent optical communication experiments"
    - "constellation shaping for wdm systems using 256qam 1024qam with probabilistic optimization"

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"
    - "nonlinear interference mitigation via deep neural networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (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"
    - "power of deep learning for channel estimation and signal detection in ofdm systems"
    - "radio transformer networks attention models for learning to synchronize in wireless systems"

Connected through hyperedge HE-6 (induced by source paper "deep learning for iot big data and streaming analytics a survey"):
  with members:
    - "on accurate and reliable anomaly detection for gas turbine combustors a deep learning approach"
    - "enabling cognitive smart cities using big data and machine learning approaches and challenges"
    - "semisupervised deep reinforcement learning in support of iot and smart city services"
    - "in datacenter performance analysis of a tensor processing unit"
    - "an analysis of deep neural network models for practical applications"
    - "approxdbn approximate computing for discriminative deep belief networks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "wireless interference identification with convolutional neural networks"

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)