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: "data free knowledge distillation for deep neural networks"
  Abstract: Recent advances in model compression have provided procedures for compressing large neural networks to a fraction of their original size while retaining most if not all of their accuracy. However, all of these approaches rely on access to the original training set, which might not always be possible if the network to be compressed was trained on a very large dataset, or on a dataset whose release poses privacy or safety concerns as may be the case for biometrics tasks. We present a method for data-free knowledge distillation, which is able to compress deep neural networks trained on large-scale datasets to a fraction of their size leveraging only some extra metadata to be provided with a pretrained model release. We also explore different kinds of metadata that can be used with our method, and discuss tradeoffs involved in using each of them.

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"
    - "zero shot knowledge distillation in deep networks"

Connected through hyperedge HE-2 (induced by source paper "dataset distillation"):
  with members:
    - "network dissection quantifying interpretability of deep visual representations"
    - "few shot adversarial domain adaptation"
    - "data distillation towards omni supervised learning"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "towards poisoning of deep learning algorithms with back gradient optimization"
    - "data poisoning attacks on factorization based collaborative filtering"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "understanding deep image representations by inverting them"

Connected through hyperedge HE-3 (induced by source paper "dreaming to distill data free knowledge transfer via deepinversion"):
  with members:
    - "hardware guided symbiotic training for compact accurate yet execution efficient lstm"
    - "adversarial neural network inversion via auxiliary knowledge alignment"
    - "relational knowledge distillation"
    - "refine and distill exploiting cycle inconsistency and knowledge distillation for unsupervised monocular depth estimation"
    - "variational information distillation for knowledge transfer"
    - "data free learning of student networks"
    - "importance estimation for neural network pruning"
    - "how good is my gan"

Connected through hyperedge HE-4 (induced by source paper "highlight every step knowledge distillation via collaborative teaching"):
  with members:
    - "towards optimal structured cnn pruning via generative adversarial learning"
    - "pcc net perspective crowd counting via spatial convolutional network"
    - "model compression via distillation and quantization"
    - "private model compression via knowledge distillation"
    - "amalgamating knowledge towards comprehensive classification"
    - "improving fast segmentation with teacher student learning"
    - "label refinery improving imagenet classification through label progression"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"

Connected through hyperedge HE-5 (induced by source paper "and the bit goes down revisiting the quantization of neural networks"):
  with members:
    - "searching for mobilenetv3"
    - "mixmatch a holistic approach to semi supervised learning"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "training wide residual networks for deployment using a single bit for each weight"
    - "a survey on methods and theories of quantized neural networks"
    - "haq hardware aware automated quantization with mixed precision"

Connected through hyperedge HE-6 (induced by source paper "relational knowledge distillation"):
  with members:
    - "object part attention model for fine grained image classification"
    - "deep metric learning with bier boosting independent embeddings robustly"
    - "model compression via distillation and quantization"
    - "learning to navigate for fine grained classification"
    - "attention based ensemble for deep metric learning"
    - "label refinery improving imagenet classification through label progression"
    - "sampling matters in deep embedding learning"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"

Connected through hyperedge HE-7 (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"
    - "zero shot knowledge distillation in deep networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"

Connected through hyperedge HE-8 (induced by source paper "zero shot knowledge transfer via adversarial belief matching"):
  with members:
    - "dataset distillation"
    - "variational information distillation for knowledge transfer"
    - "zero shot knowledge distillation in deep networks"
    - "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"

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)