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: "self supervised knowledge distillation using singular value decomposition"
  Abstract: To solve deep neural network (DNN)'s huge training dataset and its high computation issue, so-called teacher-student (T-S) DNN which transfers the knowledge of T-DNN to S-DNN has been proposed. However, the existing T-S-DNN has limited range of use, and the knowledge of T-DNN is insufficiently transferred to S-DNN. To improve the quality of the transferred knowledge from T-DNN, we propose a new knowledge distillation using singular value decomposition (SVD). In addition, we define a knowledge transfer as a self-supervised task and suggest a way to continuously receive information from T-DNN. Simulation results show that a S-DNN with a computational cost of 1/5 of the T-DNN can be up to 1.1\% better than the T-DNN in terms of classification accuracy. Also assuming the same computational cost, our S-DNN outperforms the S-DNN driven by the state-of-the-art distillation with a performance advantage of 1.79\%. code is available on this https URL\_SVD.

Connected through hyperedge HE-1 (induced by source paper "knowledge distillation via adaptive instance normalization"):
  with members:
    - "on the efficacy of knowledge distillation"
    - "relational knowledge distillation"
    - "adaptis adaptive instance selection network"
    - "few shot adversarial learning of realistic neural talking head models"
    - "xnor net improved binary neural networks"
    - "a comprehensive overhaul of feature distillation"
    - "contrastive representation distillation"
    - "similarity preserving knowledge distillation"

Connected through hyperedge HE-2 (induced by source paper "unpaired multi modal segmentation via knowledge distillation"):
  with members:
    - "evaluation of algorithms for multi modality whole heart segmentation an open access grand challenge"
    - "soft labeling by distilling anatomical knowledge for improved ms lesion segmentation"
    - "translating and segmenting multimodal medical volumes with cycle and shape consistency generative adversarial network"
    - "group normalization"
    - "ivd net intervertebral disc localization and segmentation in mri with a multi modal unet"
    - "hyperdense net a hyper densely connected cnn for multi modal image segmentation"
    - "multisource transfer learning with convolutional neural networks for lung pattern analysis"
    - "universal representations the missing link between faces text planktons and cat breeds"

Connected through hyperedge HE-3 (induced by source paper "residual knowledge distillation"):
  with members:
    - "esrgan enhanced super resolution generative adversarial networks"
    - "an embarrassingly simple approach for knowledge distillation"
    - "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"
    - "paying more attention to attention improving the performance of convolutional neural networks via attention transfer"

Connected through hyperedge HE-4 (induced by source paper "training quantized neural networks with the full precision auxiliary module"):
  with members:
    - "effective training of convolutional neural networks with low bitwidth weights and activations"
    - "circulant binary convolutional networks enhancing the performance of 1 bit dcnns with circulant back propagation"
    - "relational knowledge distillation"
    - "bi real net enhancing the performance of 1 bit cnns with improved representational capability and advanced training algorithm"
    - "yolov3 an incremental improvement"
    - "proxquant quantized neural networks via proximal operators"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"
    - "relaxed quantization for discretized neural networks"

Connected through hyperedge HE-5 (induced by source paper "graph based knowledge distillation by multi head self attention network"):
  with members:
    - "low resolution face recognition in the wild via selective knowledge distillation"
    - "relational inductive biases deep learning and graph networks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "knowledge transfer via distillation of activation boundaries formed by hidden neurons"
    - "linguistically informed self attention for semantic role labeling"
    - "non local neural networks"
    - "attention is all you need"
    - "long short term memory networks for machine reading"

Connected through hyperedge HE-6 (induced by source paper "distilling knowledge from a deep pose regressor network"):
  with members:
    - "learning monocular visual odometry through geometry aware curriculum learning"
    - "deeptam deep tracking and mapping"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "model compression via distillation and quantization"
    - "ganvo unsupervised deep monocular visual odometry and depth estimation with generative adversarial networks"
    - "thinet a filter level pruning method for deep neural network compression"
    - "deepvo towards end to end visual odometry with deep recurrent convolutional neural networks"
    - "deepiot compressing deep neural network structures for sensing systems with a compressor critic framework"

Connected through hyperedge HE-7 (induced by source paper "bypass enhancement rgb stream model for pedestrian action recognition of autonomous vehicles"):
  with members:
    - "graph based knowledge distillation by multi head self attention network"
    - "hallucinating optical flow features for video classification"
    - "relational knowledge distillation"
    - "multi label image classification via knowledge distillation from weakly supervised detection"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "compressed video action recognition"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "hidden two stream convolutional networks for action recognition"

Connected through hyperedge HE-8 (induced by source paper "an embarrassingly simple approach for knowledge distillation"):
  with members:
    - "knowledge distillation by on the fly native ensemble"
    - "self referenced deep learning"
    - "large scale distributed neural network training through online distillation"
    - "adversarial network compression"
    - "progressive neural networks"
    - "progressive growing of gans for improved quality stability and variation"
    - "focal loss for dense object detection"
    - "deep mutual 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)