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: "parameter efficient training of deep convolutional neural networks by dynamic sparse reparameterization"
  Abstract: Modern deep neural networks are typically highly overparameterized. Pruning techniques are able to remove a significant fraction of network parameters with little loss in accuracy. Recently, techniques based on dynamic reallocation of non-zero parameters have emerged, allowing direct training of sparse networks without having to pre-train a large dense model. Here we present a novel dynamic sparse reparameterization method that addresses the limitations of previous techniques such as high computational cost and the need for manual configuration of the number of free parameters allocated to each layer. We evaluate the performance of dynamic reallocation methods in training deep convolutional networks and show that our method outperforms previous static and dynamic reparameterization methods, yielding the best accuracy for a fixed parameter budget, on par with accuracies obtained by iteratively pruning a pre-trained dense model. We further investigated the mechanisms underlying the superior generalization performance of the resultant sparse networks. We found that neither the structure, nor the initialization of the non-zero parameters were sufficient to explain the superior performance. Rather, effective learning crucially depended on the continuous exploration of the sparse network structure space during training. Our work suggests that exploring structural degrees of freedom during training is more effective than adding extra parameters to the network.

Connected through hyperedge HE-1 (induced by source paper "soft threshold weight reparameterization for learnable sparsity"):
  with members:
    - "rigging the lottery making all tickets winners"
    - "sparse networks from scratch faster training without losing performance"
    - "discovering neural wirings"
    - "fastgrnn a fast accurate stable and tiny kilobyte sized gated recurrent neural network"
    - "fast sparse convnets"
    - "butterfly transform an efficient fft based neural architecture design"
    - "green ai"
    - "pytorch an imperative style high performance deep learning library"

Connected through hyperedge HE-2 (induced by source paper "campfire compressible regularization free structured sparse training for hardware accelerators"):
  with members:
    - "low memory neural network training a technical report"
    - "prunetrain gradual structured pruning from scratch for faster neural network training"
    - "full deep neural network training on a pruned weight budget"
    - "the state of sparsity in deep neural networks"
    - "scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "network compression using correlation analysis of layer responses"
    - "learning efficient convolutional networks through network slimming"

Connected through hyperedge HE-3 (induced by source paper "picking winning tickets before training by preserving gradient flow"):
  with members:
    - "pre defined sparse neural networks with hardware acceleration"
    - "eigendamage structured pruning in the kronecker factored eigenbasis"
    - "sparse networks from scratch faster training without losing performance"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "a signal propagation perspective for pruning neural networks at initialization"
    - "the difficulty of training sparse neural networks"
    - "scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"

Connected through hyperedge HE-4 (induced by source paper "low memory neural network training a technical report"):
  with members:
    - "traditional and heavy tailed self regularization in neural network models"
    - "pay less attention with lightweight and dynamic convolutions"
    - "learning fast algorithms for linear transforms using butterfly factorizations"
    - "trained quantization thresholds for accurate and efficient fixed point inference of deep neural networks"
    - "memory efficient adaptive optimization"
    - "sparse evolutionary deep learning with over one million artificial neurons on commodity hardware"
    - "fixup initialization residual learning without normalization"
    - "full deep neural network training on a pruned weight budget"

Connected through hyperedge HE-5 (induced by source paper "on the downstream performance of compressed word embeddings"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "tensorized embedding layers for efficient model compression"
    - "low memory neural network training a technical report"
    - "the state of sparsity in deep neural networks"
    - "high accuracy low precision training"
    - "low precision random fourier features for memory constrained kernel approximation"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "deep contextualized word representations"

Connected through hyperedge HE-6 (induced by source paper "rigging the lottery making all tickets winners"):
  with members:
    - "deconstructing lottery tickets zeros signs and the supermask"
    - "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"
    - "learning sparse neural networks via sensitivity driven regularization"

Connected through hyperedge HE-7 (induced by source paper "cross channel intragroup sparsity neural network"):
  with members:
    - "igcv3 interleaved low rank group convolutions for efficient deep neural networks"
    - "dnn dataflow choice is overrated"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "understanding the disharmony between dropout and batch normalization by variance shift"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "channelnets compact and efficient convolutional neural networks via channel wise convolutions"
    - "block sparse recurrent neural networks"
    - "soft filter pruning for accelerating deep convolutional neural networks"

Connected through hyperedge HE-8 (induced by source paper "word2ket space efficient word embeddings inspired by quantum entanglement"):
  with members:
    - "on the downstream performance of compressed word embeddings"
    - "low memory neural network training a technical report"
    - "generating long sequences with sparse transformers"
    - "improving sequence to sequence learning via optimal transport"
    - "roberta a robustly optimized bert pretraining approach"
    - "the state of sparsity in deep neural networks"
    - "texar a modularized versatile and extensible toolkit for text generation"
    - "high accuracy low precision training"

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)