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: "automatically generate steganographic text based on markov model and huffman coding"
  Abstract: Steganography, as one of the three basic information security systems, has long played an important role in safeguarding the privacy and confidentiality of data in cyberspace. The text is the most widely used information carrier in people's daily life, using text as a carrier for information hiding has broad research prospects. However, due to the high coding degree and less information redundancy in the text, it has been an extremely challenging problem to hide information in it for a long time. In this paper, we propose a steganography method which can automatically generate steganographic text based on the Markov chain model and Huffman coding. It can automatically generate fluent text carrier in terms of secret information which need to be embedded. The proposed model can learn from a large number of samples written by people and obtain a good estimate of the statistical language model. We evaluated the proposed model from several perspectives. Experimental results show that the performance of the proposed model is superior to all the previous related methods in terms of information imperceptibility and information hidden capacity.

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"
    - "how to augment a small learning set for improving the performances of a cnn based steganalyzer"
    - "generating steganographic text with lstms"
    - "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"
    - "generating steganographic text with lstms"
    - "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 "behavioral security in covert communication systems"):
  with members:
    - "real time steganalysis for stream media based on multi channel convolutional sliding windows"
    - "ts rnn text steganalysis based on recurrent neural networks"
    - "ts cnn text steganalysis from semantic space based on convolutional neural network"
    - "lost in the digital wild hiding information in digital activities"
    - "aag stega automatic audio generation based steganography"

Connected through hyperedge HE-4 (induced by source paper "istego100k large scale image steganalysis dataset"):
  with members:
    - "real time steganalysis for stream media based on multi channel convolutional sliding windows"
    - "ts rnn text steganalysis based on recurrent neural networks"
    - "aag stega automatic audio generation based steganography"
    - "deep convolutional neural network to detect j uniward"

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.CR(Cryptography and Security)