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 via distillation of activation boundaries formed by hidden neurons"
  Abstract: An activation boundary for a neuron refers to a separating hyperplane that determines whether the neuron is activated or deactivated. It has been long considered in neural networks that the activations of neurons, rather than their exact output values, play the most important role in forming classification friendly partitions of the hidden feature space. However, as far as we know, this aspect of neural networks has not been considered in the literature of knowledge transfer. In this paper, we propose a knowledge transfer method via distillation of activation boundaries formed by hidden neurons. For the distillation, we propose an activation transfer loss that has the minimum value when the boundaries generated by the student coincide with those by the teacher. Since the activation transfer loss is not differentiable, we design a piecewise differentiable loss approximating the activation transfer loss. By the proposed method, the student learns a separating boundary between activation region and deactivation region formed by each neuron in the teacher. Through the experiments in various aspects of knowledge transfer, it is verified that the proposed method outperforms the current state-of-the-art.

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 "supermix supervising the mixing data augmentation"):
  with members:
    - "cutmix regularization strategy to train strong classifiers with localizable features"
    - "population based augmentation efficient learning of augmentation policy schedules"
    - "relational knowledge distillation"
    - "randaugment practical automated data augmentation with a reduced search space"
    - "randaugment practical data augmentation with no separate search"
    - "variational information distillation for knowledge transfer"
    - "fast autoaugment"
    - "contrastive representation distillation"

Connected through hyperedge HE-3 (induced by source paper "distilling portable generative adversarial networks for image translation"):
  with members:
    - "compressing gans using knowledge distillation"
    - "multimodal unsupervised image to image translation"
    - "drit diverse image to image translation via disentangled representations"
    - "progressive growing of gans for improved quality stability and variation"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "stargan unified generative adversarial networks for multi domain image to image translation"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "perceptual losses for real time style transfer and super resolution"

Connected through hyperedge HE-4 (induced by source paper "subclass distillation"):
  with members:
    - "relational knowledge distillation"
    - "momentum contrast for unsupervised visual representation learning"
    - "variational information distillation for knowledge transfer"
    - "contrastive representation distillation"
    - "patient knowledge distillation for bert model compression"
    - "similarity preserving knowledge distillation"
    - "self training with noisy student improves imagenet classification"
    - "paraphrasing complex network network compression via factor transfer"

Connected through hyperedge HE-5 (induced by source paper "adversarial based knowledge distillation for multi model ensemble and noisy data refinement"):
  with members:
    - "cutmix regularization strategy to train strong classifiers with localizable features"
    - "relational knowledge distillation"
    - "towards instance level image to image translation"
    - "snapshot distillation teacher student optimization in one generation"
    - "knowledge distillation by on the fly native ensemble"
    - "multimodal unsupervised image to image translation"
    - "large scale distributed neural network training through online distillation"
    - "meal multi model ensemble via adversarial learning"

Connected through hyperedge HE-6 (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"
    - "knowledge transfer with jacobian matching"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "preparing lessons improve knowledge distillation with better supervision"):
  with members:
    - "relational knowledge distillation"
    - "variational information distillation for knowledge transfer"
    - "highlight every step knowledge distillation via collaborative teaching"
    - "be your own teacher improve the performance of convolutional neural networks via self distillation"
    - "revisit knowledge distillation a teacher free framework"
    - "similarity preserving knowledge distillation"
    - "adaptive regularization of labels"
    - "cinic 10 is not imagenet or cifar 10"

Connected through hyperedge HE-8 (induced by source paper "contrastive representation distillation"):
  with members:
    - "contrastive multiview coding"
    - "relational knowledge distillation"
    - "correlation congruence for knowledge distillation"
    - "variational information distillation for knowledge transfer"
    - "similarity preserving knowledge distillation"
    - "a theoretical analysis of contrastive unsupervised representation learning"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "paraphrasing complex network network compression via factor transfer"

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)