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: "do deep nets really need to be deep"
  Abstract: Currently, deep neural networks are the state of the art on problems such as speech recognition and computer vision. In this extended abstract, we show that shallow feed-forward networks can learn the complex functions previously learned by deep nets and achieve accuracies previously only achievable with deep models. Moreover, in some cases the shallow neural nets can learn these deep functions using a total number of parameters similar to the original deep model. We evaluate our method on the TIMIT phoneme recognition task and are able to train shallow fully-connected nets that perform similarly to complex, well-engineered, deep convolutional architectures. Our success in training shallow neural nets to mimic deeper models suggests that there probably exist better algorithms for training shallow feed-forward nets than those currently available.

Connected through hyperedge HE-1 (induced by source paper "distilled hierarchical neural ensembles with adaptive inference cost"):
  with members:
    - "adaptative inference cost with convolutional neural mixture models"
    - "depth adaptive transformer"
    - "hydra an ensemble of convolutional neural networks for geospatial land classification"
    - "robust training with ensemble consensus"
    - "deep tree learning for zero shot face anti spoofing"
    - "improved techniques for training adaptive deep networks"
    - "searching for mobilenetv3"
    - "universally slimmable networks and improved training techniques"

Connected through hyperedge HE-2 (induced by source paper "cocopie making mobile ai sweet as pie compression compilation co design goes a long way"):
  with members:
    - "patdnn achieving real time dnn execution on mobile devices with pattern based weight pruning"
    - "pconv the missing but desirable sparsity in dnn weight pruning for real time execution on mobile devices"
    - "tvm an automated end to end optimizing compiler for deep learning"
    - "wide activation for efficient and accurate image super resolution"
    - "multi style generative network for real time transfer"
    - "extremely low bit neural network squeeze the last bit out with admm"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "thinet a filter level pruning method for deep neural network compression"

Connected through hyperedge HE-3 (induced by source paper "the microsoft toolkit of multi task deep neural networks for natural language understanding"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "distilbert a distilled version of bert smaller faster cheaper and lighter"
    - "multi task deep neural networks for natural language understanding"
    - "smart robust and efficient fine tuning for pre trained natural language models through principled regularized optimization"
    - "improving multi task deep neural networks via knowledge distillation for natural language understanding"
    - "freelb enhanced adversarial training for language understanding"

Connected through hyperedge HE-4 (induced by source paper "spatiotemporal knowledge distillation for efficient estimation of aerial video saliency"):
  with members:
    - "salient object detection in the deep learning era an in depth survey"
    - "adversarial spatio temporal learning for video deblurring"
    - "deep visual attention prediction"
    - "revisiting video saliency a large scale benchmark and a new model"
    - "video salient object detection via fully convolutional networks"
    - "how drones look crowdsourced knowledge transfer for aerial video saliency prediction"
    - "deep mutual learning"
    - "exploiting inter image similarity and ensemble of extreme learners for fixation prediction using deep features"

Connected through hyperedge HE-5 (induced by source paper "lightweight 3d human pose estimation network training using teacher student learning"):
  with members:
    - "learnable triangulation of human pose"
    - "improved training of binary networks for human pose estimation and image recognition"
    - "self supervised learning of 3d human pose using multi view geometry"
    - "xnect real time multi person 3d human pose estimation with a single rgb camera"
    - "deep high resolution representation learning for human pose estimation"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "ordinal depth supervision for 3d human pose estimation"
    - "mobilenetv2 inverted residuals and linear bottlenecks"

Connected through hyperedge HE-6 (induced by source paper "mse optimal neural network initialization via layer fusion"):
  with members:
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "channel charting locating users within the radio environment using channel state information"
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "a convergence theory for deep learning via over parameterization"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"

Connected through hyperedge HE-7 (induced by source paper "an efficient method of training small models for regression problems with knowledge distillation"):
  with members:
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "wing loss for robust facial landmark localisation with convolutional neural networks"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "mpiigaze real world dataset and deep appearance based gaze estimation"
    - "learning from noisy large scale datasets with minimal supervision"
    - "learning from noisy labels with distillation"
    - "understanding deep learning requires rethinking generalization"

Connected through hyperedge HE-8 (induced by source paper "residual knowledge distillation"):
  with members:
    - "esrgan enhanced super resolution generative adversarial networks"
    - "an embarrassingly simple approach for knowledge distillation"
    - "self supervised knowledge distillation using singular value decomposition"
    - "adversarial network compression"
    - "cascade residual learning a two stage convolutional neural network for stereo matching"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "like what you like knowledge distill via neuron selectivity transfer"
    - "n2n learning network to network compression via policy gradient reinforcement learning"

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)