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: "an exploration of parameter redundancy in deep networks with circulant projections"
  Abstract: We explore the redundancy of parameters in deep neural networks by replacing the conventional linear projection in fully-connected layers with the circulant projection. The circulant structure substantially reduces memory footprint and enables the use of the Fast Fourier Transform to speed up the computation. Considering a fully-connected neural network layer with d input nodes, and d output nodes, this method improves the time complexity from O(d^2) to O(dlogd) and space complexity from O(d^2) to O(d). The space savings are particularly important for modern deep convolutional neural network architectures, where fully-connected layers typically contain more than 90% of the network parameters. We further show that the gradient computation and optimization of the circulant projections can be performed very efficiently. Our experiments on three standard datasets show that the proposed approach achieves this significant gain in storage and efficiency with minimal increase in error rate compared to neural networks with unstructured projections.

Connected through hyperedge HE-1 (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-2 (induced by source paper "centripetal sgd for pruning very deep convolutional networks with complicated structure"):
  with members:
    - "towards compact convnets via structure sparsity regularized filter pruning"
    - "autopruner an end to end trainable filter pruning method for efficient deep model inference"
    - "leveraging filter correlations for deep model compression"
    - "discrimination aware channel pruning for deep neural networks"
    - "a systematic dnn weight pruning framework using alternating direction method of multipliers"
    - "structured pruning for efficient convnets via incremental regularization"
    - "soft filter pruning for accelerating deep convolutional neural networks"
    - "rethinking the value of network pruning"

Connected through hyperedge HE-3 (induced by source paper "cascaded projection end to end network compression and acceleration"):
  with members:
    - "learning to prune filters in convolutional neural networks"
    - "amc automl for model compression and acceleration on mobile devices"
    - "nvidia tensor core programmability performance precision"
    - "yolov3 an incremental improvement"
    - "discrimination aware channel pruning for deep neural networks"
    - "soft filter pruning for accelerating deep convolutional neural networks"
    - "nisp pruning networks using neuron importance score propagation"
    - "an analysis of deep neural network models for practical applications"

Connected through hyperedge HE-4 (induced by source paper "patient knowledge distillation for bert model compression"):
  with members:
    - "multilingual neural machine translation with knowledge distillation"
    - "improving multi task deep neural networks via knowledge distillation for natural language understanding"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "roberta a robustly optimized bert pretraining approach"
    - "sdnet contextualized attention based deep network for conversational question answering"
    - "model compression via distillation and quantization"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "deep contextualized word representations"

Connected through hyperedge HE-5 (induced by source paper "dynamic multi path neural network"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "slimmable neural networks"
    - "model compression via distillation and quantization"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "quantization mimic towards very tiny cnn for object detection"
    - "skipnet learning dynamic routing in convolutional networks"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "quantization and training of neural networks for efficient integer arithmetic only inference"

Connected through hyperedge HE-6 (induced by source paper "sipping neural networks sensitivity informed provable pruning of neural networks"):
  with members:
    - "revisiting hard thresholding for dnn pruning"
    - "on coresets for logistic regression"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "data dependent coresets for compressing neural networks with applications to generalization bounds"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "snip single shot network pruning based on connection sensitivity"
    - "rethinking the value of network pruning"
    - "training support vector machines using coresets"

Connected through hyperedge HE-7 (induced by source paper "compressing rnns for iot devices by 15 38x using kronecker products"):
  with members:
    - "tucker tensor layer in fully connected neural networks"
    - "run time efficient rnn compression for inference on edge devices"
    - "fastgrnn a fast accurate stable and tiny kilobyte sized gated recurrent neural network"
    - "measuring scheduling efficiency of rnns for nlp applications"
    - "the state of sparsity in deep neural networks"
    - "structured weight matrices based hardware accelerators in deep neural networks fpgas and asics"
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "stabilizing gradients for deep neural networks via efficient svd parameterization"

Connected through hyperedge HE-8 (induced by source paper "self binarizing networks"):
  with members:
    - "effnet an efficient structure for convolutional neural networks"
    - "deep learning towards mobile applications"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "towards accurate binary convolutional neural network"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "hashnet deep learning to hash by continuation"
    - "incremental network quantization towards lossless cnns with low precision weights"
    - "gxnor net training deep neural networks with ternary weights and activations without full precision memory under a unified discretization framework"

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.CV(Computer Vision and Pattern Recognition)