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: "automatic node selection for deep neural networks using group lasso regularization"
  Abstract: We examine the effect of the Group Lasso (gLasso) regularizer in selecting the salient nodes of Deep Neural Network (DNN) hidden layers by applying a DNN-HMM hybrid speech recognizer to TED Talks speech data. We test two types of gLasso regularization, one for outgoing weight vectors and another for incoming weight vectors, as well as two sizes of DNNs: 2048 hidden layer nodes and 4096 nodes. Furthermore, we compare gLasso and L2 regularizers. Our experiment results demonstrate that our DNN training, in which the gLasso regularizer was embedded, successfully selected the hidden layer nodes that are necessary and sufficient for achieving high classification power.

Connected through hyperedge HE-1 (induced by source paper "an improving framework of regularization for network compression"):
  with members:
    - "a survey of model compression and acceleration for deep neural networks"
    - "learning structured sparsity in deep neural networks"
    - "paying more attention to attention improving the performance of convolutional neural networks via attention transfer"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "learning the number of neurons in deep networks"
    - "pruning filters for efficient convnets"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "convolutional neural networks with low rank regularization"

Connected through hyperedge HE-2 (induced by source paper "optimizing for interpretability in deep neural networks with tree regularization"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "methods for interpreting and understanding deep neural networks"
    - "explanation in artificial intelligence insights from the social sciences"
    - "right for the right reasons training differentiable models by constraining their explanations"
    - "distilling a neural network into a soft decision tree"
    - "an unexpected unity among methods for interpreting model predictions"
    - "from softmax to sparsemax a sparse model of attention and multi label classification"
    - "harnessing deep neural networks with logic rules"

Connected through hyperedge HE-3 (induced by source paper "castnet community attentive spatio temporal networks for opioid overdose forecasting"):
  with members:
    - "group anomaly detection using deep generative models"
    - "a dual stage attention based recurrent neural network for time series prediction"
    - "deep forecast deep learning based spatio temporal forecasting"
    - "attention is all you need"
    - "co occurrence feature learning for skeleton based action recognition using regularized deep lstm 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.CL(Computation and Language)