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: "compact and computationally efficient representation of deep neural networks"
  Abstract: At the core of any inference procedure in deep neural networks are dot product operations, which are the component that require the highest computational resources. A common approach to reduce the cost of inference is to reduce its memory complexity by lowering the entropy of the weight matrices of the neural network, e.g., by pruning and quantizing their elements. However, the quantized weight matrices are then usually represented either by a dense or sparse matrix storage format, whose associated dot product complexity is not bounded by the entropy of the matrix. This means that the associated inference complexity ultimately depends on the implicit statistical assumptions that these matrix representations make about the weight distribution, which can be in many cases suboptimal. In this paper we address this issue and present new efficient representations for matrices with low entropy statistics. These new matrix formats have the novel property that their memory and algorithmic complexity are implicitly bounded by the entropy of the matrix, consequently implying that they are guaranteed to become more efficient as the entropy of the matrix is being reduced. In our experiments we show that performing the dot product under these new matrix formats can indeed be more energy and time efficient under practically relevant assumptions. For instance, we are able to attain up to x42 compression ratios, x5 speed ups and x90 energy savings when we convert in a lossless manner the weight matrices of state-of-the-art networks such as AlexNet, VGG-16, ResNet152 and DenseNet into the new matrix formats and benchmark their respective dot product operation.

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"
    - "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"
    - "universal deep neural network compression"
    - "entropy constrained training of deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "pruning by explaining a novel criterion for deep neural network pruning"):
  with members:
    - "unmasking clever hans predictors and assessing what machines really learn"
    - "importance estimation for neural network pruning"
    - "methods for interpreting and understanding deep neural networks"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "nisp pruning networks using neuron importance score propagation"
    - "building fast and compact convolutional neural networks for offline handwritten chinese character recognition"
    - "deep learning with convolutional neural networks for eeg decoding and visualization"
    - "structured probabilistic pruning for convolutional neural network acceleration"

Connected through hyperedge HE-4 (induced by source paper "robust and communication efficient federated learning from non iid data"):
  with members:
    - "towards federated learning at scale system design"
    - "deep neural networks for no reference and full reference image quality assessment"
    - "signsgd with majority vote is communication efficient and fault tolerant"
    - "federated learning strategies for improving communication efficiency"
    - "sparse binary compression towards distributed deep learning with minimal communication"
    - "variance based gradient compression for efficient distributed deep learning"
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "how to backdoor federated learning"

Connected through hyperedge HE-5 (induced by source paper "deepcabac context adaptive binary arithmetic coding for deep neural network compression"):
  with members:
    - "robust and communication efficient federated learning from non iid data"
    - "deep neural network approximation for custom hardware where we ve been where we re going"
    - "sparse binary compression towards distributed deep learning with minimal communication"
    - "entropy constrained training of deep neural networks"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "critical learning periods in deep neural networks"
    - "a survey of model compression and acceleration for deep neural networks"
    - "federated learning of deep networks using model averaging"

Connected through hyperedge HE-6 (induced by source paper "multi kernel prediction networks for denoising of burst images"):
  with members:
    - "iterative residual cnns for burst photography applications"
    - "entropy constrained training of deep neural networks"
    - "learning to see in the dark"
    - "unprocessing images for learned raw denoising"
    - "deep burst denoising"
    - "video frame interpolation via adaptive convolution"
    - "burst denoising with kernel prediction networks"
    - "medical image denoising using convolutional denoising autoencoders"

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"
    - "sparse binary compression towards distributed deep learning with minimal communication"
    - "methods for interpreting and understanding deep neural networks"
    - "universal deep neural network compression"
    - "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 "sparse binary compression towards distributed deep learning with minimal communication"):
  with members:
    - "error feedback fixes signsgd and other gradient compression schemes"
    - "robust and communication efficient federated learning from non iid data"
    - "unmasking clever hans predictors and assessing what machines really learn"
    - "federated learning strategies for improving communication efficiency"
    - "variance based gradient compression for efficient distributed deep learning"
    - "sparsified sgd with memory"
    - "signsgd compressed optimisation for non convex problems"
    - "deep gradient compression reducing the communication bandwidth for distributed 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)