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 transfer via adversarial belief matching"
  Abstract: Performing knowledge transfer from a large teacher network to a smaller student is a popular task in modern deep learning applications. However, due to growing dataset sizes and stricter privacy regulations, it is increasingly common not to have access to the data that was used to train the teacher. We propose a novel method which trains a student to match the predictions of its teacher without using any data or metadata. We achieve this by training an adversarial generator to search for images on which the student poorly matches the teacher, and then using them to train the student. Our resulting student closely approximates its teacher for simple datasets like SVHN, and on CIFAR10 we improve on the state-of-the-art for few-shot distillation (with 100 images per class), despite using no data. Finally, we also propose a metric to quantify the degree of belief matching between teacher and student in the vicinity of decision boundaries, and observe a significantly higher match between our zero-shot student and the teacher, than between a student distilled with real data and the teacher. Code available at: this https URL

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"
    - "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"
    - "mobilenetv2 inverted residuals and linear bottlenecks"

Connected through hyperedge HE-2 (induced by source paper "noisy machines understanding noisy neural networks and enhancing robustness to analog hardware errors using distillation"):
  with members:
    - "adversarially robust distillation"
    - "accurate deep neural network inference using computational phase change memory"
    - "fixynn efficient hardware for mobile computer vision via transfer learning"
    - "neuromorphic computing with multi memristive synapses"
    - "mixed precision in memory computing"
    - "all optical machine learning using diffractive deep neural networks"
    - "model compression via distillation and quantization"
    - "rxnn a framework for evaluating deep neural networks on resistive crossbars"

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"
    - "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"
    - "dawn dynamic adversarial watermarking of neural networks"

Connected through hyperedge HE-4 (induced by source paper "high accuracy and high fidelity extraction of neural networks"):
  with members:
    - "s4l self supervised semi supervised learning"
    - "a framework for the extraction of deep neural networks by leveraging public data"
    - "overlearning reveals sensitive attributes"
    - "rethinking deep active learning using unlabeled data at model training"
    - "combining mixmatch and active learning for better accuracy with fewer labels"
    - "on the learnability of deep random networks"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "mixmatch a holistic approach to semi supervised learning"

Connected through hyperedge HE-5 (induced by source paper "high fidelity extraction of neural network models"):
  with members:
    - "s4l self supervised semi supervised learning"
    - "a framework for the extraction of deep neural networks by leveraging public data"
    - "overlearning reveals sensitive attributes"
    - "on the learnability of deep random networks"
    - "mixmatch a holistic approach to semi supervised learning"
    - "energy and policy considerations for deep learning in nlp"
    - "knockoff nets stealing functionality of black box models"
    - "copycat cnn stealing knowledge by persuading confession with random non labeled data"

Connected through hyperedge HE-6 (induced by source paper "eternal sunshine of the spotless net selective forgetting in deep networks"):
  with members:
    - "where is the information in a deep neural network"
    - "knock knock who s there membership inference on aggregate location data"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "computing nonvacuous generalization bounds for deep stochastic neural networks with many more parameters than training data"
    - "logan membership inference attacks against generative models"
    - "machine learning models that remember too much"
    - "deep models under the gan information leakage from collaborative deep learning"
    - "understanding deep learning requires rethinking generalization"

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)