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: "contrastive representation distillation"
  Abstract: Often we wish to transfer representational knowledge from one neural network to another. Examples include distilling a large network into a smaller one, transferring knowledge from one sensory modality to a second, or ensembling a collection of models into a single estimator. Knowledge distillation, the standard approach to these problems, minimizes the KL divergence between the probabilistic outputs of a teacher and student network. We demonstrate that this objective ignores important structural knowledge of the teacher network. This motivates an alternative objective by which we train a student to capture significantly more information in the teacher's representation of the data. We formulate this objective as contrastive learning. Experiments demonstrate that our resulting new objective outperforms knowledge distillation and other cutting-edge distillers on a variety of knowledge transfer tasks, including single model compression, ensemble distillation, and cross-modal transfer. Our method sets a new state-of-the-art in many transfer tasks, and sometimes even outperforms the teacher network when combined with knowledge distillation. Code: this http URL

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"
    - "similarity preserving knowledge distillation"
    - "mobilenetv2 inverted residuals and linear bottlenecks"

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"
    - "smoothfool an efficient framework for computing smooth adversarial perturbations"

Connected through hyperedge HE-3 (induced by source paper "subclass distillation"):
  with members:
    - "relational knowledge distillation"
    - "momentum contrast for unsupervised visual representation learning"
    - "variational information distillation for knowledge transfer"
    - "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"
    - "large scale distributed neural network training through online distillation"

Connected through hyperedge HE-4 (induced by source paper "knapsack pruning with inner distillation"):
  with members:
    - "network pruning via transformable architecture search"
    - "approximated oracle filter pruning for destructive cnn width optimization"
    - "importance estimation for neural network pruning"
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "filter pruning via geometric median for deep convolutional neural networks acceleration"
    - "amc automl for model compression and acceleration on mobile devices"
    - "rethinking the smaller norm less informative assumption in channel pruning of convolution layers"
    - "soft filter pruning for accelerating deep convolutional neural networks"

Connected through hyperedge HE-5 (induced by source paper "contrastive multiview coding"):
  with members:
    - "learning robust and multilingual speech representations"
    - "self supervised learning of video induced visual invariances"
    - "end to end learning of visual representations from uncurated instructional videos"
    - "momentum contrast for unsupervised visual representation learning"
    - "large scale adversarial representation learning"
    - "randaugment practical data augmentation with no separate search"
    - "a survey of multi view representation learning"
    - "data efficient image recognition with contrastive predictive coding"

Connected through hyperedge HE-6 (induced by source paper "quest quantized embedding space for transferring knowledge"):
  with members:
    - "on the efficacy of knowledge distillation"
    - "relational knowledge distillation"
    - "variational information distillation for knowledge transfer"
    - "revisit knowledge distillation a teacher free framework"
    - "similarity preserving knowledge distillation"
    - "paraphrasing complex network network compression via factor transfer"
    - "learning deep representations with probabilistic knowledge transfer"
    - "knowledge transfer via distillation of activation boundaries formed by hidden neurons"

Connected through hyperedge HE-7 (induced by source paper "the state of knowledge distillation for classification"):
  with members:
    - "extreme language model compression with optimal subwords and shared projections"
    - "relational knowledge distillation"
    - "randaugment practical automated data augmentation with a reduced search space"
    - "randaugment practical data augmentation with no separate search"
    - "a comprehensive overhaul of feature distillation"
    - "unsupervised data augmentation for consistency training"
    - "tinybert distilling bert for natural language understanding"
    - "patient knowledge distillation for bert model compression"

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)