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: "memory efficient implementation of densenets"
  Abstract: The DenseNet architecture is highly computationally efficient as a result of feature reuse. However, a naive DenseNet implementation can require a significant amount of GPU memory: If not properly managed, pre-activation batch normalization and contiguous convolution operations can produce feature maps that grow quadratically with network depth. In this technical report, we introduce strategies to reduce the memory consumption of DenseNets during training. By strategically using shared memory allocations, we reduce the memory cost for storing feature maps from quadratic to linear. Without the GPU memory bottleneck, it is now possible to train extremely deep DenseNets. Networks with 14M parameters can be trained on a single GPU, up from 4M. A 264-layer DenseNet (73M parameters), which previously would have been infeasible to train, can now be trained on a single workstation with 8 NVIDIA Tesla M40 GPUs. On the ImageNet ILSVRC classification dataset, this large DenseNet obtains a state-of-the-art single-crop top-1 error of 20.26%.

Connected through hyperedge HE-1 (induced by source paper "rnnpool efficient non linear pooling for ram constrained inference"):
  with members:
    - "visual wake words dataset"
    - "extd extremely tiny face detector via iterative filter reuse"
    - "lffd a light and fast face detector for edge devices"
    - "fastgrnn a fast accurate stable and tiny kilobyte sized gated recurrent neural network"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "the state of sparsity in deep neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "proxylessnas direct neural architecture search on target task and hardware"

Connected through hyperedge HE-2 (induced by source paper "efficient ladder style densenets for semantic segmentation of large images"):
  with members:
    - "in defense of pre trained imagenet architectures for real time semantic segmentation of road driving images"
    - "on the convergence of adam and beyond"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "learning a discriminative feature network for semantic segmentation"
    - "adaptive affinity fields for semantic segmentation"
    - "gated feedback refinement network for coarse to fine dense semantic image labeling"
    - "on the iterative refinement of densely connected representation levels for semantic segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"

Connected through hyperedge HE-3 (induced by source paper "learning fully dense neural networks for image semantic segmentation"):
  with members:
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "focal loss for dense object detection"
    - "stacked deconvolutional network for semantic segmentation"
    - "rethinking atrous convolution for semantic image segmentation"
    - "mask r cnn"
    - "refinenet multi path refinement networks for high resolution semantic segmentation"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "densely connected convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "specnet spectral domain convolutional neural network"):
  with members:
    - "efficient memory management for gpu based deep learning systems"
    - "exploiting kernel sparsity and entropy for interpretable cnn compression"
    - "sort second order response transform for visual recognition"
    - "wavelet convolutional neural networks for texture classification"
    - "the power of sparsity in convolutional neural networks"
    - "a survey of model compression and acceleration for deep neural networks"
    - "in place activated batchnorm for memory optimized training of dnns"
    - "learning structured sparsity in deep neural networks"

Connected through hyperedge HE-5 (induced by source paper "towards understanding residual and dilated dense neural networks via convolutional sparse coding"):
  with members:
    - "on first order meta learning algorithms"
    - "densely connected convolutional networks for speech recognition"
    - "meta sgd learning to learn quickly for few shot learning"
    - "working locally thinking globally theoretical guarantees for convolutional sparse coding"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "tensorflow a system for large scale machine learning"
    - "you only look once unified real time object detection"

Connected through hyperedge HE-6 (induced by source paper "edgecnn convolutional neural network classification model with small inputs for edge computing"):
  with members:
    - "searching for mobilenetv3"
    - "mixconv mixed depthwise convolutional kernels"
    - "on lightweight privacy preserving collaborative learning for internet of things objects"
    - "lep cnn a lightweight edge device assisted privacy preserving cnn inference solution for iot"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "igcv3 interleaved low rank group convolutions for efficient deep neural networks"
    - "facial expression recognition method based on complexity perception classification algorithm"
    - "deep learning based multiple object visual tracking on embedded system for iot and mobile edge computing applications"

Connected through hyperedge HE-7 (induced by source paper "efficient memory management for gpu based deep learning systems"):
  with members:
    - "training wide residual networks for deployment using a single bit for each weight"
    - "superneurons dynamic gpu memory management for training deep neural networks"
    - "ease ml towards multi tenant resource sharing for machine learning workloads"
    - "comparative study of deep learning software frameworks"
    - "lifetime based memory management for distributed data processing systems"
    - "database meets deep learning challenges and opportunities"
    - "eie efficient inference engine on compressed deep neural network"
    - "training deep nets with sublinear memory cost"

Connected through hyperedge HE-8 (induced by source paper "a graph theoretic framework of recomputation algorithms for memory efficient backpropagation"):
  with members:
    - "superneurons dynamic gpu memory management for training deep neural networks"
    - "thinet a filter level pruning method for deep neural network compression"
    - "megdet a large mini batch object detector"
    - "mixed precision training"
    - "vdnn virtualized deep neural networks for scalable memory efficient neural network design"
    - "pyramid scene parsing network"
    - "training deep nets with sublinear memory cost"
    - "u net convolutional networks for biomedical image segmentation"

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)