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: "universal deep neural network compression"
  Abstract: Compression of deep neural networks (DNNs) for memory- and computation-efficient compact feature representations becomes a critical problem particularly for deployment of DNNs on resource-limited platforms. In this paper, we investigate lossy compression of DNNs by weight quantization and lossless source coding for memory-efficient inference. Whereas the previous work addressed non-universal scalar quantization and entropy coding of DNN weights, we for the first time introduce universal DNN compression by universal vector quantization and universal source coding. In particular, we examine universal randomized lattice quantization of DNNs, which randomizes DNN weights by uniform random dithering before lattice quantization and can perform near-optimally on any source without relying on knowledge of its probability distribution. Entropy coding schemes such as Huffman codes require prior calculation of source statistics, which is computationally consuming. Instead, we propose universal lossless source coding schemes such as variants of Lempel-Ziv-Welch or the Burrows-Wheeler transform. Finally, we present the methods of fine-tuning vector quantized DNNs to recover the performance loss after quantization. Our experimental results show that the proposed universal DNN compression scheme achieves compression ratios of 124.80, 47.10 and 42.46 for LeNet5, 32-layer ResNet and AlexNet, respectively.

Connected through hyperedge HE-1 (induced by source paper "trends and advancements in deep neural network communication"):
  with members:
    - "deepcabac a universal compression algorithm for deep neural networks"
    - "error feedback fixes signsgd and other gradient compression schemes"
    - "decentralized stochastic optimization and gossip algorithms with compressed communication"
    - "powersgd practical low rank gradient compression for distributed optimization"
    - "advances and open problems in federated learning"
    - "megatron lm training multi billion parameter language models using model parallelism"
    - "improving federated learning personalization via model agnostic meta learning"
    - "hierarchical federated learning across heterogeneous cellular networks"

Connected through hyperedge HE-2 (induced by source paper "deepcabac a universal compression algorithm for deep neural networks"):
  with members:
    - "robust and communication efficient federated learning from non iid data"
    - "unmasking clever hans predictors and assessing what machines really learn"
    - "scalable methods for 8 bit training of neural networks"
    - "compact and computationally efficient representation of deep neural networks"
    - "sparse binary compression towards distributed deep learning with minimal communication"
    - "training deep neural networks with 8 bit floating point numbers"
    - "learning sparse neural networks via sensitivity driven regularization"
    - "entropy constrained training of deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "hadanets flexible quantization strategies for neural networks"):
  with members:
    - "scalable methods for 8 bit training of neural networks"
    - "mixed precision training of convolutional neural networks using integer operations"
    - "compressing neural networks using the variational information bottleneck"
    - "xcel ram accelerating binary neural networks in high throughput sram compute arrays"
    - "superhuman accuracy on the snemi3d connectomics challenge"
    - "network sketching exploiting binary structure in deep cnns"
    - "wrpn wide reduced precision networks"
    - "the high dimensional geometry of binary neural networks"

Connected through hyperedge HE-4 (induced by source paper "jointly sparse convolutional neural networks in dual spatial winograd domains"):
  with members:
    - "compressing neural networks using the variational information bottleneck"
    - "soft to hard vector quantization for end to end learning compressible representations"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "enabling sparse winograd convolution by native pruning"
    - "towards the limit of network quantization"
    - "learning structured sparsity in deep neural networks"
    - "faster cnns with direct sparse convolutions and guided pruning"
    - "dynamic network surgery for efficient dnns"

Connected through hyperedge HE-5 (induced by source paper "learning low precision deep neural networks through regularization"):
  with members:
    - "loss aware weight quantization of deep networks"
    - "soft to hard vector quantization for end to end learning compressible representations"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "training quantized nets a deeper understanding"
    - "deep learning with low precision by half wave gaussian quantization"
    - "incremental network quantization towards lossless cnns with low precision weights"
    - "ct srcnn cascade trained and trimmed deep convolutional neural networks for image super resolution"
    - "ternary neural networks with fine grained quantization"

Connected through hyperedge HE-6 (induced by source paper "compact and computationally efficient representation of deep neural networks"):
  with members:
    - "scalable methods for 8 bit training of neural networks"
    - "deep neural networks for no reference and full reference image quality assessment"
    - "sparse binary compression towards distributed deep learning with minimal communication"
    - "methods for interpreting and understanding deep neural networks"
    - "training deep neural networks with 8 bit floating point numbers"
    - "mixed low precision deep learning inference using dynamic fixed point"
    - "explainable artificial intelligence understanding visualizing and interpreting deep learning models"
    - "gxnor net training deep neural networks with ternary weights and activations without full precision memory under a unified discretization framework"

Connected through hyperedge HE-7 (induced by source paper "entropy constrained training of deep neural networks"):
  with members:
    - "robust and communication efficient federated learning from non iid data"
    - "unmasking clever hans predictors and assessing what machines really learn"
    - "compact and computationally efficient representation of deep neural networks"
    - "sparse binary compression towards distributed deep learning with minimal communication"
    - "methods for interpreting and understanding deep neural networks"
    - "explainable artificial intelligence understanding visualizing and interpreting deep learning models"
    - "emergence of invariance and disentanglement in deep representations"
    - "learning discrete weights using the local reparameterization trick"

Connected through hyperedge HE-8 (induced by source paper "low memory convolutional neural networks through incremental depth first processing"):
  with members:
    - "brainslug transparent acceleration of deep learning through depth first parallelism"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "mec memory efficient convolution for deep neural network"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "compression aware training of deep networks"
    - "ternary weight networks"
    - "deep counter networks for asynchronous event based processing"
    - "quantized neural networks training neural networks with low precision weights and activations"

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)