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: "a robust blind watermarking using convolutional neural network"
  Abstract: This paper introduces a blind watermarking based on a convolutional neural network (CNN). We propose an iterative learning framework to secure robustness of watermarking. One loop of learning process consists of the following three stages: Watermark embedding, attack simulation, and weight update. We have learned a network that can detect a 1-bit message from a image sub-block. Experimental results show that this learned network is an extension of the frequency domain that is widely used in existing watermarking scheme. The proposed scheme achieved robustness against geometric and signal processing attacks with a learning time of one day.

Connected through hyperedge HE-1 (induced by source paper "distortion agnostic deep watermarking"):
  with members:
    - "deep learning for channel coding via neural mutual information estimation"
    - "romark a robust watermarking system using adversarial training"
    - "steganogan high capacity image steganography with gans"
    - "robust invisible video watermarking with attention"
    - "adversarial training for free"
    - "stegastamp invisible hyperlinks in physical photographs"
    - "redmark framework for residual diffusion watermarking on deep networks"
    - "neural joint source channel coding"

Connected through hyperedge HE-2 (induced by source paper "digital passport a novel technological strategy for intellectual property protection of convolutional neural networks"):
  with members:
    - "deepmarks a digital fingerprinting framework for deep neural networks"
    - "turning your weakness into a strength watermarking deep neural networks by backdooring"
    - "hidden hiding data with deep networks"
    - "deepsigns a generic watermarking framework for ip protection of deep learning models"
    - "embedding watermarks into deep neural networks"
    - "adversarial frontier stitching for remote neural network watermarking"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-3 (induced by source paper "romark a robust watermarking system using adversarial training"):
  with members:
    - "redmark framework for residual diffusion watermarking on deep networks"
    - "hidden hiding data with deep networks"
    - "adversarial machine learning at scale"
    - "intriguing properties of neural networks"

Connected through hyperedge HE-4 (induced by source paper "hidden hiding data with deep networks"):
  with members:
    - "learning to protect communications with adversarial neural cryptography"
    - "generating steganographic text with lstms"
    - "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-5 (induced by source paper "redmark framework for residual diffusion watermarking on deep networks"):
  with members:
    - "deep residual learning for image recognition"
    - "fast and accurate deep network learning by exponential linear units elus"

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.MM(Multimedia)