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: "alternating direction method of multipliers for sparse convolutional neural networks"
  Abstract: The storage and computation requirements of Convolutional Neural Networks (CNNs) can be prohibitive for exploiting these models over low-power or embedded devices. This paper reduces the computational complexity of the CNNs by minimizing an objective function, including the recognition loss that is augmented with a sparsity-promoting penalty term. The sparsity structure of the network is identified using the Alternating Direction Method of Multipliers (ADMM), which is widely used in large optimization problems. This method alternates between promoting the sparsity of the network and optimizing the recognition performance, which allows us to exploit the two-part structure of the corresponding objective functions. In particular, we take advantage of the separability of the sparsity-inducing penalty functions to decompose the minimization problem into sub-problems that can be solved sequentially. Applying our method to a variety of state-of-the-art CNN models, our proposed method is able to simplify the original model, generating models with less computation and fewer parameters, while maintaining and often improving generalization performance. Accomplishments on a variety of models strongly verify that our proposed ADMM-based method can be a very useful tool for simplifying and improving deep CNNs.

Connected through hyperedge HE-1 (induced by source paper "a convergence analysis of nonlinearly constrained admm in deep learning"):
  with members:
    - "deep component analysis via alternating direction neural networks"
    - "fenchel lifted networks a lagrange relaxation of neural network training"
    - "training neural networks without gradients a scalable admm approach"
    - "constructive neural network learning"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "a systematic dnn weight pruning framework using alternating direction method of multipliers"):
  with members:
    - "extremely low bit neural network squeeze the last bit out with admm"
    - "thinet a filter level pruning method for deep neural network compression"
    - "fine pruning joint fine tuning and compression of a convolutional network with bayesian optimization"
    - "learning to prune deep neural networks via layer wise optimal brain surgeon"
    - "nest a neural network synthesis tool based on a grow and prune paradigm"
    - "learning structured sparsity in deep neural networks"
    - "dynamic network surgery for efficient dnns"
    - "designing energy efficient convolutional neural networks using energy aware pruning"

Connected through hyperedge HE-3 (induced by source paper "bitnet bit regularized deep neural networks"):
  with members:
    - "soft to hard vector quantization for end to end learning compressible representations"
    - "mixed low precision deep learning inference using dynamic fixed point"
    - "fixed point optimization of deep neural networks with adaptive step size retraining"
    - "two bit networks for deep learning on resource constrained embedded devices"
    - "towards the limit of network quantization"
    - "dorefa net training low bitwidth convolutional neural networks with low bitwidth gradients"
    - "deep neural networks are robust to weight binarization and other non linear distortions"
    - "understanding the impact of precision quantization on the accuracy and energy of neural networks"

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.NE(Neural and Evolutionary Computing)