human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "ts rnn text steganalysis based on recurrent neural networks"):
  Abstract: With the rapid development of natural language processing technologies, more and more text steganographic methods based on automatic text generation technology have appeared in recent years. These models use the powerful self-learning and feature extraction ability of the neural networks to learn the feature expression of massive normal texts. Then they can automatically generate dense steganographic texts which conform to such statistical distribution based on the learned statistical patterns. In this paper, we observe that the conditional probability distribution of each word in the automatically generated steganographic texts will be distorted after embedded with hidden information. We use Recurrent Neural Networks (RNNs) to extract these feature distribution differences and then classify those features into cover text and stego text categories. Experimental results show that the proposed model can achieve high detection accuracy. Besides, the proposed model can even make use of the subtle differences of the feature distribution of texts to estimate the amount of hidden information embedded in the generated steganographic text.

Includes members:
  Member "real time steganalysis for stream media based on multi channel convolutional sliding windows":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "hierarchical representation network for steganalysis of qim steganography in low bit rate speech signals")
  Member "ts cnn text steganalysis from semantic space based on convolutional neural network":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "text steganalysis with attentional lstm cnn")
  Member "clinical assistant diagnosis for electronic medical record based on convolutional neural network":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "real time steganalysis for stream media based on multi channel convolutional sliding windows")
  Member "automatically generate steganographic text based on markov model and huffman coding":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "real time steganalysis for stream media based on multi channel convolutional sliding windows")
  Member "generating steganographic text with lstms":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "real time steganalysis for stream media based on multi channel convolutional sliding windows")
  Member "image captioning with object detection and localization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "real time steganalysis for stream media based on multi channel convolutional sliding windows")
  Member "neural responding machine for short text conversation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "an empirical investigation of pre trained transformer language models for open domain dialogue generation")
  Member "adam a method for stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")

Question: Please tell me which class the source paper 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)