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 transfer with jacobian matching"
  Abstract: Classical distillation methods transfer representations from a "teacher" neural network to a "student" network by matching their output activations. Recent methods also match the Jacobians, or the gradient of output activations with the input. However, this involves making some ad hoc decisions, in particular, the choice of the loss function. #R##N#In this paper, we first establish an equivalence between Jacobian matching and distillation with input noise, from which we derive appropriate loss functions for Jacobian matching. We then rely on this analysis to apply Jacobian matching to transfer learning by establishing equivalence of a recent transfer learning procedure to distillation. #R##N#We then show experimentally on standard image datasets that Jacobian-based penalties improve distillation, robustness to noisy inputs, and transfer learning.

Connected through hyperedge HE-1 (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 distillation with adversarial samples supporting decision boundary"
    - "large scale distributed neural network training through online distillation"
    - "heated up softmax embedding"

Connected through hyperedge HE-2 (induced by source paper "self distillation amplifies regularization in hilbert space"):
  with members:
    - "hydra preserving ensemble diversity for model distillation"
    - "disentangling feature and lazy learning in deep neural networks an empirical study"
    - "relational knowledge distillation"
    - "variational information distillation for knowledge transfer"
    - "knowledge distillation by on the fly native ensemble"
    - "a closer look at deep learning heuristics learning rate restarts warmup and distillation"
    - "like what you like knowledge distill via neuron selectivity transfer"
    - "distral robust multitask reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "deeply supervised knowledge synergy"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "dropblock a regularization method for convolutional networks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "deep supervision with intermediate concepts"
    - "deep co training for semi supervised image recognition"
    - "deeptravel a neural network based travel time estimation model with auxiliary supervision"
    - "modality distillation with multiple stream networks for action recognition"
    - "efficient neural architecture search via parameter sharing"

Connected through hyperedge HE-4 (induced by source paper "shrinkteanet million scale lightweight face recognition via shrinking teacher student networks"):
  with members:
    - "fishnet a versatile backbone for image region and pixel level prediction"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "paraphrasing complex network network compression via factor transfer"
    - "arcface additive angular margin loss for deep face recognition"
    - "learning towards minimum hyperspherical energy"
    - "multicolumn networks for face recognition"
    - "mobilefacenets efficient cnns for accurate real time face verification on mobile devices"
    - "knowledge transfer via distillation of activation boundaries formed by hidden neurons"

Connected through hyperedge HE-5 (induced by source paper "efficient convolutional neural networks for depth based multi person pose estimation"):
  with members:
    - "learning to detect and track visible and occluded body joints in a virtual world"
    - "real time convolutional networks for depth based human pose estimation"
    - "densepose dense human pose estimation in the wild"
    - "domain adaptation for visual applications a comprehensive survey"
    - "an analysis of deep neural network models for practical applications"
    - "cycada cycle consistent adversarial domain adaptation"
    - "lstm pose machines"
    - "v2v posenet voxel to voxel prediction network for accurate 3d hand and human pose estimation from a single depth map"

Connected through hyperedge HE-6 (induced by source paper "full gradient representation for neural network visualization"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "a theoretical explanation for perplexing behaviors of backpropagation based visualizations"
    - "knowledge transfer via distillation of activation boundaries formed by hidden neurons"
    - "sanity checks for saliency maps"
    - "evaluating feature importance estimates"
    - "smoothgrad removing noise by adding noise"
    - "explaining nonlinear classification decisions with deep taylor decomposition"
    - "axiomatic attribution for deep networks"

Connected through hyperedge HE-7 (induced by source paper "a comprehensive overhaul of feature distillation"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "paraphrasing complex network network compression via factor transfer"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "knowledge transfer via distillation of activation boundaries formed by hidden neurons"
    - "nsml meet the mlaas platform with a real world case study"
    - "batch renormalization towards reducing minibatch dependence in batch normalized models"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "mixup beyond empirical risk minimization"

Connected through hyperedge HE-8 (induced by source paper "ensemble knowledge distillation for learning improved and efficient networks"):
  with members:
    - "paraphrasing complex network network compression via factor transfer"
    - "knowledge distillation with adversarial samples supporting decision boundary"
    - "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)