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: "stegexpose a tool for detecting lsb steganography"
  Abstract: Steganalysis tools play an important part in saving time and providing new angles of attack for forensic analysts. StegExpose is a solution designed for use in the real world, and is able to analyse images for LSB steganography in bulk using proven attacks in a time efficient manner. When steganalytic methods are combined intelligently, they are able generate even more accurate results. This is the prime focus of StegExpose.

Connected through hyperedge HE-1 (induced by source paper "steganogan high capacity image steganography with gans"):
  with members:
    - "stegnet mega image steganography capacity with deep convolutional network"
    - "hidden hiding data with deep networks"
    - "began boundary equilibrium generative adversarial networks"
    - "progressive growing of gans for improved quality stability and variation"
    - "stacked generative adversarial networks"
    - "high resolution image inpainting using multi scale neural patch synthesis"
    - "energy based generative adversarial network"
    - "densely connected convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "pixelsteganalysis destroying hidden information with a low degree of visual degradation"):
  with members:
    - "spatial image steganography based on generative adversarial network"
    - "convolutional video steganography with temporal residual modeling"
    - "stegnet mega image steganography capacity with deep convolutional network"
    - "ssgan secure steganography based on generative adversarial networks"
    - "an improvement on lsb matching and lsb matching revisited steganography methods"
    - "pixelcnn improving the pixelcnn with discretized logistic mixture likelihood and other modifications"
    - "steganographic generative adversarial networks"
    - "unsupervised steganalysis based on artificial training sets"

Connected through hyperedge HE-3 (induced by source paper "basn learning steganography with binary attention mechanism"):
  with members:
    - "camera style adaptation for person re identification"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "you only look once unified real time object detection"
    - "deep residual learning for image recognition"
    - "facenet a unified embedding for face recognition and clustering"
    - "fast r cnn"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "stegnet mega image steganography capacity with deep convolutional network"):
  with members:
    - "began boundary equilibrium generative adversarial networks"
    - "ssgan secure steganography based on generative adversarial networks"
    - "steganographic generative adversarial networks"
    - "squeeze and excitation networks"
    - "aggregated residual transformations for deep neural networks"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "xception deep learning with depthwise separable convolutions"
    - "fully convolutional instance aware semantic segmentation"

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)