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: "model compression by entropy penalized reparameterization"
  Abstract: We describe an end-to-end neural network weight compression approach that draws inspiration from recent latent-variable data compression methods. The network parameters (weights and biases) are represented in a "latent" space, amounting to a reparameterization. This space is equipped with a learned probability model, which is used to impose an entropy penalty on the parameter representation during training, and to compress the representation using arithmetic coding after training. We are thus maximizing accuracy and model compressibility jointly, in an end-to-end fashion, with the rate--error trade-off specified by a hyperparameter. We evaluate our method by compressing six distinct model architectures on the MNIST, CIFAR-10 and ImageNet classification benchmarks. Our method achieves state-of-the-art compression on VGG-16, LeNet300-100 and several ResNet architectures, and is competitive on LeNet-5.

Connected through hyperedge HE-1 (induced by source paper "structured multi hashing for model compression"):
  with members:
    - "data free quantization through weight equalization and bias correction"
    - "searching for mobilenetv3"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "non discriminative data or weak model on the relative importance of data and model resolution"
    - "measuring the intrinsic dimension of objective landscapes"
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "amc automl for model compression and acceleration on mobile devices"
    - "regularized evolution for image classifier architecture search"

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.LG(Machine Learning)