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: "dropback continuous pruning during training"
  Abstract: We introduce a technique that compresses deep neural networks both during and after training by constraining the total number of weights updated during backpropagation to those with the highest total gradients. The remaining weights are forgotten and their initial value is regenerated at every access to avoid storing them in memory. This dramatically reduces the number of off-chip memory accesses during both training and inference, a key component of the energy needs of DNN accelerators. #R##N#By ensuring that the total weight diffusion remains close to that of baseline unpruned SGD, networks pruned using DropBack are able to maintain high accuracy across network architectures. We observe weight compression of 25x with LeNet-300-100 on MNIST while maintaining accuracy. On CIFAR-10, we see an approximately 5x weight compression on 3 models: an already 9x-reduced VGG-16, Densenet, and WRN-28-10 - all with zero or negligible accuracy loss. On Densenet and WRN, which are particularly challenging to compress, Both Densenet and WRN improve on the state of the art, achieving higher compression with better accuracy than prior pruning techniques.

Connected through hyperedge HE-1 (induced by source paper "accelerated cnn training through gradient approximation"):
  with members:
    - "training simplification and model simplification for deep learning a minimal effort back propagation method"
    - "prunetrain gradual structured pruning from scratch for faster neural network training"
    - "prunetrain fast neural network training by dynamic sparse model reconfiguration"
    - "biologically plausible learning algorithms can scale to large datasets"
    - "nvidia tensor core programmability performance precision"
    - "faster neural network training with approximate tensor operations"
    - "sbnet sparse blocks network for fast inference"
    - "assessing the scalability of biologically motivated deep learning algorithms and architectures"

Connected through hyperedge HE-2 (induced by source paper "explaining deep learning models using causal inference"):
  with members:
    - "causal learning and explanation of deep neural networks via autoencoded activations"
    - "gender bias in neural natural language processing"
    - "a primer on causal analysis"
    - "deep convolutional neural networks for computer aided detection cnn architectures dataset characteristics and transfer learning"
    - "deep residual learning for image recognition"
    - "learning deep features for discriminative localization"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-3 (induced by source paper "droppruning for model compression"):
  with members:
    - "an analysis of deep neural network models for practical applications"
    - "a survey of model compression and acceleration for deep neural networks"
    - "learning to prune deep neural networks via layer wise optimal brain surgeon"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "compressing neural networks with the hashing trick"
    - "do deep nets really need to be deep"

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)