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: "jpeg image compression using discrete cosine transform a survey"
  Abstract: Due to the increasing requirements for transmission of images in computer, mobile environments, the research in the field of image compression has increased significantly. Image compression plays a crucial role in digital image processing, it is also very important for efficient transmission and storage of images. When we compute the number of bits per image resulting from typical sampling rates and quantization methods, we find that Image compression is needed. Therefore development of efficient techniques for image compression has become necessary .This paper is a survey for lossy image compression using Discrete Cosine Transform, it covers JPEG compression algorithm which is used for full-colour still image applications and describes all the components of it.

Connected through hyperedge HE-1 (induced by source paper "an efficient pre processing method to eliminate adversarial effects"):
  with members:
    - "image transformation can make neural networks more robust against adversarial examples"
    - "comdefend an efficient image compression model to defend adversarial examples"
    - "keeping the bad guys out protecting and vaccinating deep learning with jpeg compression"
    - "defense against adversarial attacks using high level representation guided denoiser"
    - "pixeldefend leveraging generative models to understand and defend against adversarial examples"
    - "mitigating adversarial effects through randomization"
    - "practical black box attacks against machine learning"
    - "towards evaluating the robustness of neural networks"

Connected through hyperedge HE-2 (induced by source paper "a study for image compression using re pair algorithm"):
  with members:
    - "sse lossless compression method for the text of the insignificance of the lines order"

Connected through hyperedge HE-3 (induced by source paper "vehicles detection based on background modeling"):
  with members:
    - "background image generation using boolean operations"

Connected through hyperedge HE-4 (induced by source paper "energy efficient time domain vector by matrix multiplier for neurocomputing and beyond"):
  with members:
    - "multiscale co design analysis of energy latency area and accuracy of a reram analog neural training accelerator"
    - "in datacenter performance analysis of a tensor processing unit"
    - "temperature insensitive analog vector by matrix multiplier based on 55 nm nor flash memory cells"
    - "sub 1 us sub 20 nj pattern classification in a mixed signal circuit based on embedded 180 nm floating gate memory cell arrays"
    - "quantized neural networks training neural networks with low precision weights and activations"
    - "precise deep neural network computation on imprecise low power analog hardware"

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)