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: "generating steganographic text with lstms"
  Abstract: Motivated by concerns for user privacy, we design a steganographic system ("stegosystem") that enables two users to exchange encrypted messages without an adversary detecting that such an exchange is taking place. We propose a new linguistic stegosystem based on a Long Short-Term Memory (LSTM) neural network. We demonstrate our approach on the Twitter and Enron email datasets and show that it yields high-quality steganographic text while significantly improving capacity (encrypted bits per word) relative to the state-of-the-art.

Connected through hyperedge HE-1 (induced by source paper "real time steganalysis for stream media based on multi channel convolutional sliding windows"):
  with members:
    - "clinical assistant diagnosis for electronic medical record based on convolutional neural network"
    - "automatically generate steganographic text based on markov model and huffman coding"
    - "how to augment a small learning set for improving the performances of a cnn based steganalyzer"
    - "a novel convolutional neural network for image steganalysis with shared normalization"
    - "image captioning with object detection and localization"
    - "densely connected convolutional networks"
    - "fractalnet ultra deep neural networks without residuals"
    - "bag of tricks for efficient text classification"

Connected through hyperedge HE-2 (induced by source paper "ts rnn text steganalysis based on recurrent neural networks"):
  with members:
    - "real time steganalysis for stream media based on multi channel convolutional sliding windows"
    - "ts cnn text steganalysis from semantic space based on convolutional neural network"
    - "clinical assistant diagnosis for electronic medical record based on convolutional neural network"
    - "automatically generate steganographic text based on markov model and huffman coding"
    - "image captioning with object detection and localization"
    - "neural responding machine for short text conversation"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-3 (induced by source paper "text steganalysis with attentional lstm cnn"):
  with members:
    - "hierarchical representation network for steganalysis of qim steganography in low bit rate speech signals"
    - "ts cnn text steganalysis from semantic space based on convolutional neural network"
    - "attention is all you need"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "convolutional neural networks for sentence classification"

Connected through hyperedge HE-4 (induced by source paper "neural linguistic steganography"):
  with members:
    - "abstractive text summarization using sequence to sequence rnns and beyond"
    - "sequence to sequence rnns for text summarization"
    - "teaching machines to read and comprehend"

Connected through hyperedge HE-5 (induced by source paper "towards near imperceptible steganographic text"):
  with members:
    - "attention is all you need"
    - "exploring the limits of language modeling"
    - "neural machine translation of rare words with subword units"

Connected through hyperedge HE-6 (induced by source paper "hidden hiding data with deep networks"):
  with members:
    - "a robust blind watermarking using convolutional neural network"
    - "learning to protect communications with adversarial neural cryptography"
    - "embedding watermarks into deep neural networks"
    - "practical black box attacks against machine learning"
    - "unsupervised steganalysis based on artificial training sets"
    - "adversarial examples in the physical world"
    - "adam a method for stochastic optimization"
    - "intriguing properties of neural networks"

Connected through hyperedge HE-7 (induced by source paper "aag stega automatic audio generation based steganography"):
  with members:
    - "music transcription modelling and composition using deep learning"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-8 (induced by source paper "ts cnn text steganalysis from semantic space based on convolutional neural network"):
  with members:
    - "convolutional neural networks for sentence classification"

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.AI(Artificial Intelligence)