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: "cae p compressive autoencoder with pruning based on admm"
  Abstract: Since compressive autoencoder (CAE) was proposed, autoencoder, as a simple and efficient neural network model, has outperformed traditional codecs such as JPEG, JPEG 2000 etc. in lossy image compression. However, it faces the problem that the bitrate, characterizing the compression ratio, cannot be optimized by general methods due to its discreteness. Current research additionally trains an entropy estimator to indirectly optimize the bitrate. In this paper, we proposed the compressive autoencoder with pruning based on ADMM (CAE-P) which replaces the traditionally used entropy estimating technique with ADMM-based pruning method inspired by the field of neural network architecture search and avoided the extra effort needed for training an entropy estimator. Our experiments show that this pruning paradigm helps the CAE-P yield a better result compared with the original CAE along with other traditional codecs when measured in both SSIM and MS-SSIM. We further explored the applied pruning method by looking into the detail of the latent codes learned by CAE-P to examine its effectiveness.

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.CV(Computer Vision and Pattern Recognition)