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: "predicting parameters in deep learning"
  Abstract: We demonstrate that there is significant redundancy in the parameterization of several deep learning models. Given only a few weight values for each feature it is possible to accurately predict the remaining values. Moreover, we show that not only can the parameter values be predicted, but many of them need not be learned at all. We train several different architectures by learning only a small number of weights and predicting the rest. In the best case we are able to predict more than 95% of the weights of a network without any drop in accuracy.

Connected through hyperedge HE-1 (induced by source paper "exploiting the redundancy in convolutional filters for parameter reduction"):
  with members:
    - "drop an octave reducing spatial redundancy in convolutional neural networks with octave convolution"
    - "learning filter basis for convolutional neural network compression"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "efficient neural architecture search via parameter sharing"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-2 (induced by source paper "lightweight residual densely connected convolutional neural network"):
  with members:
    - "denser deep evolutionary network structured representation"
    - "indoor scene understanding in 2 5 3d for autonomous agents a survey"
    - "residual dense network for image restoration"
    - "convolutional neural networks with alternately updated clique"
    - "competitive inner imaging squeeze and excitation for residual network"
    - "residual dense network for image super resolution"
    - "skipnet learning dynamic routing in convolutional networks"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"

Connected through hyperedge HE-3 (induced by source paper "spatiotemporal knowledge distillation for efficient estimation of aerial video saliency"):
  with members:
    - "salient object detection in the deep learning era an in depth survey"
    - "adversarial spatio temporal learning for video deblurring"
    - "deep visual attention prediction"
    - "revisiting video saliency a large scale benchmark and a new model"
    - "video salient object detection via fully convolutional networks"
    - "how drones look crowdsourced knowledge transfer for aerial video saliency prediction"
    - "deep mutual learning"
    - "exploiting inter image similarity and ensemble of extreme learners for fixation prediction using deep features"

Connected through hyperedge HE-4 (induced by source paper "bert of theseus compressing bert by progressive module replacing"):
  with members:
    - "are sixteen heads really better than one"
    - "reducing transformer depth on demand with structured dropout"
    - "distilbert a distilled version of bert smaller faster cheaper and lighter"
    - "q bert hessian based ultra low precision quantization of bert"
    - "well read students learn better on the importance of pre training compact models"
    - "deep double descent where bigger models and more data hurt"
    - "multilingual neural machine translation with knowledge distillation"
    - "mass masked sequence to sequence pre training for language generation"

Connected through hyperedge HE-5 (induced by source paper "hrank filter pruning using high rank feature map"):
  with members:
    - "filter sketch for network pruning"
    - "channel pruning via automatic structure search"
    - "towards optimal structured cnn pruning via generative adversarial learning"
    - "filter pruning via geometric median for deep convolutional neural networks acceleration"
    - "revisiting the importance of individual units in cnns via ablation"
    - "recent advances in efficient computation of deep convolutional neural networks"
    - "learning efficient convolutional networks through network slimming"
    - "nisp pruning networks using neuron importance score propagation"

Connected through hyperedge HE-6 (induced by source paper "parameter efficient transfer from sequential behaviors for user profiling and recommendation"):
  with members:
    - "future data helps training modeling future contexts for session based recommendation"
    - "parameter efficient transfer learning for nlp"
    - "albert a lite bert for self supervised learning of language representations"
    - "darec deep domain adaptation for cross domain recommendation via transferring rating patterns"
    - "bert and pals projected attention layers for efficient adaptation in multi task learning"
    - "vl bert pre training of generic visual linguistic representations"
    - "do better imagenet models transfer better"
    - "conet collaborative cross networks for cross domain recommendation"

Connected through hyperedge HE-7 (induced by source paper "structured compression and sharing of representational space for continual learning"):
  with members:
    - "learn to grow a continual structure learning framework for overcoming catastrophic forgetting"
    - "continual lifelong learning with neural networks a review"
    - "gradient based sample selection for online continual learning"
    - "re evaluating continual learning scenarios a categorization and case for strong baselines"
    - "tree cnn a deep convolutional neural network for lifelong learning"
    - "a low effort approach to structured cnn design using pca"
    - "alleviating catastrophic forgetting using context dependent gating and synaptic stabilization"
    - "generative replay with feedback connections as a general strategy for continual learning"

Connected through hyperedge HE-8 (induced by source paper "super resolution using segmentation prior self attention generative adversarial network"):
  with members:
    - "expectation maximization attention networks for semantic segmentation"
    - "image super resolution using attention based densenet with residual deconvolution"
    - "recovering realistic texture in image super resolution by deep spatial feature transform"
    - "image super resolution using very deep residual channel attention networks"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "attention u net learning where to look for the pancreas"
    - "dual attention network for scene segmentation"
    - "deep laplacian pyramid networks for fast and accurate super resolution"

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)