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: "learning sparse neural networks via sensitivity driven regularization"
  Abstract: The ever-increasing number of parameters in deep neural networks poses challenges for memory-limited applications. Regularize-and-prune methods aim at meeting these challenges by sparsifying the network weights. In this context we quantify the output sensitivity to the parameters (i.e. their relevance to the network output) and introduce a regularization term that gradually lowers the absolute value of parameters with low sensitivity. Thus, a very large fraction of the parameters approach zero and are eventually set to zero by simple thresholding. Our method surpasses most of the recent techniques both in terms of sparsity and error rates. In some cases, the method reaches twice the sparsity obtained by other techniques at equal error rates.

Connected through hyperedge HE-1 (induced by source paper "deepcabac a universal compression algorithm for deep neural networks"):
  with members:
    - "robust and communication efficient federated learning from non iid data"
    - "unmasking clever hans predictors and assessing what machines really learn"
    - "scalable methods for 8 bit training of neural networks"
    - "compact and computationally efficient representation of deep neural networks"
    - "sparse binary compression towards distributed deep learning with minimal communication"
    - "training deep neural networks with 8 bit floating point numbers"
    - "universal deep neural network compression"
    - "entropy constrained training of deep neural networks"

Connected through hyperedge HE-2 (induced by source paper "rigging the lottery making all tickets winners"):
  with members:
    - "deconstructing lottery tickets zeros signs and the supermask"
    - "parameter efficient training of deep convolutional neural networks by dynamic sparse reparameterization"
    - "the state of sparsity in deep neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "efficient neural audio synthesis"
    - "snip single shot network pruning based on connection sensitivity"

Connected through hyperedge HE-3 (induced by source paper "group connected multilayer perceptron networks"):
  with members:
    - "dynamic feature acquisition using denoising autoencoders"
    - "nutrition and health data for cost sensitive learning"
    - "characterizing sparse connectivity patterns in neural networks"
    - "scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science"
    - "feature grouping as a stochastic regularizer for high dimensional structured data"
    - "how powerful are graph neural networks"
    - "self normalizing neural networks"
    - "semi supervised classification with graph convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "post synaptic potential regularization has potential"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "regularization for deep learning a taxonomy"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "entropy sgd biasing gradient descent into wide valleys"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"

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)