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: "entropy constrained training of deep neural networks"
  Abstract: We propose a general framework for neural network compression that is motivated by the Minimum Description Length (MDL) principle. For that we first derive an expression for the entropy of a neural network, which measures its complexity explicitly in terms of its bit-size. Then, we formalize the problem of neural network compression as an entropy-constrained optimization objective. This objective generalizes many of the compression techniques proposed in the literature, in that pruning or reducing the cardinality of the weight elements of the network can be seen special cases of entropy-minimization techniques. Furthermore, we derive a continuous relaxation of the objective, which allows us to minimize it using gradient based optimization techniques. Finally, we show that we can reach state-of-the-art compression results on different network architectures and data sets, e.g. achieving x71 compression gains on a VGG-like architecture.

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"
    - "universal deep neural network compression"

Connected through hyperedge HE-3 (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"
    - "compact and computationally efficient representation of deep neural networks"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "using latent codes for class imbalance problem in unsupervised domain adaptation"):
  with members:
    - "learning to transfer examples for partial domain adaptation"
    - "cgans with projection discriminator"
    - "improving adversarial discriminative domain adaptation"
    - "open set domain adaptation by backpropagation"
    - "deep visual domain adaptation a survey"
    - "importance weighted adversarial nets for partial domain adaptation"
    - "partial adversarial domain adaptation"
    - "learning to reweight examples for robust deep learning"

Connected through hyperedge HE-5 (induced by source paper "scalable model compression by entropy penalized reparameterization"):
  with members:
    - "deepcabac context adaptive binary arithmetic coding for deep neural network compression"
    - "uniq uniform noise injection for the quantization of neural networks"
    - "uniq uniform noise injection for non uniform quantization of neural networks"
    - "relaxed quantization for discretized neural networks"
    - "coreset based neural network compression"
    - "end to end optimized image compression"
    - "ternary weight networks"
    - "identity mappings in deep residual networks"

Connected through hyperedge HE-6 (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"
    - "compact and computationally efficient representation of deep neural networks"
    - "sparse binary compression towards distributed deep learning with minimal communication"
    - "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-7 (induced by source paper "multi kernel prediction networks for denoising of burst images"):
  with members:
    - "compact and computationally efficient representation of deep neural networks"
    - "iterative residual cnns for burst photography applications"
    - "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"

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)