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 as constrained optimization with application to neural nets part i general framework"
  Abstract: Compressing neural nets is an active research problem, given the large size of state-of-the-art nets for tasks such as object recognition, and the computational limits imposed by mobile devices. We give a general formulation of model compression as constrained optimization. This includes many types of compression: quantization, low-rank decomposition, pruning, lossless compression and others. Then, we give a general algorithm to optimize this nonconvex problem based on the augmented Lagrangian and alternating optimization. This results in a "learning-compression" algorithm, which alternates a learning step of the uncompressed model, independent of the compression type, with a compression step of the model parameters, independent of the learning task. This simple, efficient algorithm is guaranteed to find the best compressed model for the task in a local sense under standard assumptions. #R##N#We present separately in several companion papers the development of this general framework into specific algorithms for model compression based on quantization, pruning and other variations, including experimental results on compressing neural nets and other models.

Connected through hyperedge HE-1 (induced by source paper "a programmable approach to model compression"):
  with members:
    - "amc automl for model compression and acceleration on mobile devices"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "discrimination aware channel pruning for deep neural networks"
    - "progressive deep neural networks acceleration via soft filter pruning"
    - "adam admm a unified systematic framework of structured weight pruning for dnns"
    - "attention is all you need"
    - "thinet a filter level pruning method for deep neural network compression"
    - "condensenet an efficient densenet using learned group convolutions"

Connected through hyperedge HE-2 (induced by source paper "proxquant quantized neural networks via proximal operators"):
  with members:
    - "alternating multi bit quantization for recurrent neural networks"
    - "on the universal approximability and complexity bounds of quantized relu neural networks"
    - "loss aware weight quantization of deep networks"
    - "binaryrelax a relaxation approach for training deep neural networks with quantized weights"
    - "the high dimensional geometry of binary neural networks"
    - "training quantized nets a deeper understanding"
    - "model compression as constrained optimization with application to neural nets part ii quantization"
    - "dorefa net training low bitwidth convolutional neural networks with low bitwidth gradients"

Connected through hyperedge HE-3 (induced by source paper "binaryrelax a relaxation approach for training deep neural networks with quantized weights"):
  with members:
    - "stochastic layer wise precision in deep neural networks"
    - "extremely low bit neural network squeeze the last bit out with admm"
    - "training quantized nets a deeper understanding"
    - "model compression as constrained optimization with application to neural nets part ii quantization"
    - "deep learning with low precision by half wave gaussian quantization"
    - "incremental network quantization towards lossless cnns with low precision weights"
    - "deep relaxation partial differential equations for optimizing deep neural networks"
    - "sep nets small and effective pattern networks"

Connected through hyperedge HE-4 (induced by source paper "channel wise pruning of neural networks with tapering resource constraint"):
  with members:
    - "faster gaze prediction with dense networks and fisher pruning"
    - "rethinking the smaller norm less informative assumption in channel pruning of convolution layers"
    - "a systematic dnn weight pruning framework using alternating direction method of multipliers"
    - "progressive deep neural networks acceleration via soft filter pruning"
    - "systematic weight pruning of dnns using alternating direction method of multipliers"
    - "the incredible shrinking neural network new perspectives on learning representations through the lens of pruning"
    - "structured probabilistic pruning for convolutional neural network acceleration"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-5 (induced by source paper "generalized ternary connect end to end learning and compression of multiplication free deep neural networks"):
  with members:
    - "training and inference with integers in deep neural networks"
    - "model compression via distillation and quantization"
    - "wrpn apprentice methods for training and inference using low precision numerics"
    - "apprentice using knowledge distillation techniques to improve low precision network accuracy"
    - "model compression as constrained optimization with application to neural nets part ii quantization"
    - "bitnet bit regularized deep neural networks"
    - "convolutional neural networks using logarithmic data representation"
    - "binaryconnect training deep neural networks with binary weights during propagations"

Connected through hyperedge HE-6 (induced by source paper "model compression as constrained optimization with application to neural nets part ii quantization"):
  with members:
    - "dorefa net training low bitwidth convolutional neural networks with low bitwidth gradients"
    - "ternary weight networks"
    - "quantized neural networks training neural networks with low precision weights and activations"
    - "theano a python framework for fast computation of mathematical expressions"
    - "binaryconnect training deep neural networks with binary weights during propagations"
    - "learning both weights and connections for efficient neural networks"
    - "caffe convolutional architecture for fast feature embedding"
    - "compressing deep convolutional networks using vector quantization"

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)