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: "knowledge distillation with adversarial samples supporting decision boundary"
  Abstract: Many recent works on knowledge distillation have provided ways to transfer the knowledge of a trained network for improving the learning process of a new one, but finding a good technique for knowledge distillation is still an open problem. In this paper, we provide a new perspective based on a decision boundary, which is one of the most important component of a classifier. The generalization performance of a classifier is closely related to the adequacy of its decision boundary, so a good classifier bears a good decision boundary. Therefore, transferring information closely related to the decision boundary can be a good attempt for knowledge distillation. To realize this goal, we utilize an adversarial attack to discover samples supporting a decision boundary. Based on this idea, to transfer more accurate information about the decision boundary, the proposed algorithm trains a student classifier based on the adversarial samples supporting the decision boundary. Experiments show that the proposed method indeed improves knowledge distillation and achieves the state-of-the-arts performance.

Connected through hyperedge HE-1 (induced by source paper "search for better students to learn distilled knowledge"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "exploring randomly wired neural networks for image recognition"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "darts differentiable architecture search"
    - "understanding individual decisions of cnns via contrastive backpropagation"
    - "neural architecture search with bayesian optimisation and optimal transport"
    - "rethinking the value of network pruning"

Connected through hyperedge HE-2 (induced by source paper "understanding and improving knowledge distillation"):
  with members:
    - "billion scale semi supervised learning for image classification"
    - "when does label smoothing help"
    - "revisit knowledge distillation a teacher free framework"
    - "ranking distillation learning compact ranking models with high performance for recommender system"
    - "bag of tricks for image classification with convolutional neural networks"
    - "knowledge transfer with jacobian matching"
    - "large scale distributed neural network training through online distillation"
    - "heated up softmax embedding"

Connected through hyperedge HE-3 (induced by source paper "correlation congruence for knowledge distillation"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "the devil of face recognition is in the noise"
    - "arcface additive angular margin loss for deep face recognition"
    - "large scale distributed neural network training through online distillation"
    - "gradient descent provably optimizes over parameterized neural networks"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "deep mutual learning"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-4 (induced by source paper "dbp discrimination based block level pruning for deep model acceleration"):
  with members:
    - "towards optimal structured cnn pruning via generative adversarial learning"
    - "similarity preserving knowledge distillation"
    - "cop customized deep model compression via regularized correlation based filter level pruning"
    - "greedy layerwise learning can scale to imagenet"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "filter pruning via geometric median for deep convolutional neural networks acceleration"
    - "adversarial network compression"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"

Connected through hyperedge HE-5 (induced by source paper "towards oracle knowledge distillation with neural architecture search"):
  with members:
    - "relational knowledge distillation"
    - "detnas backbone search for object detection"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "paraphrasing complex network network compression via factor transfer"
    - "boosting self supervised learning via knowledge transfer"
    - "knowledge distillation by on the fly native ensemble"
    - "neural architecture optimization"
    - "darts differentiable architecture search"

Connected through hyperedge HE-6 (induced by source paper "knowledge representing efficient sparse representation of prior knowledge for knowledge distillation"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "learning strict identity mappings in deep residual networks"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "data driven sparse structure selection for deep neural networks"
    - "a geometric view of optimal transportation and generative model"
    - "thinet a filter level pruning method for deep neural network compression"
    - "like what you like knowledge distill via neuron selectivity transfer"
    - "a survey of model compression and acceleration for deep neural networks"

Connected through hyperedge HE-7 (induced by source paper "looking for the devil in the details learning trilinear attention sampling network for fine grained image recognition"):
  with members:
    - "large scale fine grained categorization and domain specific transfer learning"
    - "bilinear attention networks"
    - "learning to navigate for fine grained classification"
    - "learning to zoom a saliency based sampling layer for neural networks"
    - "non local neural networks"
    - "attention is all you need"
    - "towards faster training of global covariance pooling networks by iterative matrix square root normalization"
    - "dynamic computational time for visual attention"

Connected through hyperedge HE-8 (induced by source paper "ensemble knowledge distillation for learning improved and efficient networks"):
  with members:
    - "knowledge transfer with jacobian matching"
    - "paraphrasing complex network network compression via factor transfer"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "gpipe efficient training of giant neural networks using pipeline parallelism"
    - "mnasnet platform aware neural architecture search for mobile"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "nisp pruning networks using neuron importance score propagation"
    - "coupled ensembles of neural networks"

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)