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: "zero shot knowledge distillation in deep networks"
  Abstract: Knowledge distillation deals with the problem of training a smaller model (Student) from a high capacity source model (Teacher) so as to retain most of its performance. Existing approaches use either the training data or meta-data extracted from it in order to train the Student. However, accessing the dataset on which the Teacher has been trained may not always be feasible if the dataset is very large or it poses privacy or safety concerns (e.g., bio-metric or medical data). Hence, in this paper, we propose a novel data-free method to train the Student from the Teacher. Without even using any meta-data, we synthesize the Data Impressions from the complex Teacher model and utilize these as surrogates for the original training data samples to transfer its learning to Student via knowledge distillation. We, therefore, dub our method "Zero-Shot Knowledge Distillation" and demonstrate that our framework results in competitive generalization performance as achieved by distillation using the actual training data samples on multiple benchmark datasets.

Connected through hyperedge HE-1 (induced by source paper "generative low bitwidth data free quantization"):
  with members:
    - "zeroq a novel zero shot quantization framework"
    - "data free quantization through weight equalization and bias correction"
    - "effective training of convolutional neural networks with low bitwidth weights and activations"
    - "zero shot knowledge transfer via adversarial belief matching"
    - "improving neural network quantization without retraining using outlier channel splitting"
    - "data free learning of student networks"
    - "nat neural architecture transformer for accurate and compact architectures"
    - "mobilenetv2 inverted residuals and linear bottlenecks"

Connected through hyperedge HE-2 (induced by source paper "unlabeled data deployment for classification of diabetic retinopathy images using knowledge transfer"):
  with members:
    - "modeling teacher student techniques in deep neural networks for knowledge distillation"
    - "model compression via distillation and quantization"
    - "apprentice using knowledge distillation techniques to improve low precision network accuracy"
    - "zoom in net deep mining lesions for diabetic retinopathy detection"
    - "decaf a deep convolutional activation feature for generic visual recognition"

Connected through hyperedge HE-3 (induced by source paper "thieves on sesame street model extraction of bert based apis"):
  with members:
    - "boolq exploring the surprising difficulty of natural yes no questions"
    - "right for the wrong reasons diagnosing syntactic heuristics in natural language inference"
    - "high fidelity extraction of neural network models"
    - "zero shot knowledge transfer via adversarial belief matching"
    - "learning and evaluating general linguistic intelligence"
    - "a framework for the extraction of deep neural networks by leveraging public data"
    - "membership inference attacks on sequence to sequence models"
    - "prediction poisoning utility constrained defenses against model stealing attacks"

Connected through hyperedge HE-4 (induced by source paper "reconciling utility and membership privacy via knowledge distillation"):
  with members:
    - "stolen memories leveraging model memorization for calibrated white box membership inference"
    - "disparate vulnerability on the unfairness of privacy attacks against machine learning"
    - "evaluating differentially private machine learning in practice"
    - "exploiting unintended feature leakage in collaborative learning"
    - "private model compression via knowledge distillation"
    - "the secret sharer measuring unintended neural network memorization extracting secrets"
    - "i know what you see power side channel attack on convolutional neural network accelerators"
    - "ml leaks model and data independent membership inference attacks and defenses on machine learning models"

Connected through hyperedge HE-5 (induced by source paper "data free adversarial distillation"):
  with members:
    - "dafl data free learning of student networks"
    - "student becoming the master knowledge amalgamation for joint scene parsing depth estimation and more"
    - "refine and distill exploiting cycle inconsistency and knowledge distillation for unsupervised monocular depth estimation"
    - "customizing student networks from heterogeneous teachers via adaptive knowledge amalgamation"
    - "attending to discriminative certainty for domain adaptation"
    - "data free learning of student networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "esrgan enhanced super resolution generative adversarial networks"

Connected through hyperedge HE-6 (induced by source paper "zero shot knowledge transfer via adversarial belief matching"):
  with members:
    - "dataset distillation"
    - "variational information distillation for knowledge transfer"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "knowledge distillation from few samples"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "realistic evaluation of deep semi supervised learning algorithms"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "data free knowledge distillation for deep neural networks"

Connected through hyperedge HE-7 (induced by source paper "few shot network compression via cross distillation"):
  with members:
    - "structured pruning of recurrent neural networks through neuron selection"
    - "rtn reparameterized ternary network"
    - "scalable methods for 8 bit training of neural networks"
    - "proxquant quantized neural networks via proximal operators"
    - "few sample knowledge distillation for efficient network compression"
    - "knowledge distillation from few samples"
    - "rethinking the value of network pruning"
    - "thinet a filter level pruning method for deep neural network compression"

Connected through hyperedge HE-8 (induced by source paper "degan data enriching gan for retrieving representative samples from a trained classifier"):
  with members:
    - "learning from higher layer feature visualizations"
    - "ask acquire and attack data free uap generation using class impressions"
    - "few sample knowledge distillation for efficient network compression"
    - "knowledge distillation from few samples"
    - "end to end incremental learning"
    - "domain adaptation for visual applications a comprehensive survey"
    - "statistics of deep generated images"
    - "continual learning with deep generative replay"

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)