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: "high fidelity extraction of neural network models"
  Abstract: Model extraction allows an adversary to steal a copy of a remotely deployed machine learning model given access to its predictions. Adversaries are motivated to mount such attacks for a variety of reasons, ranging from reducing their computational costs, to eliminating the need to collect expensive training data, to obtaining a copy of a model in order to find adversarial examples, perform membership inference, or model inversion attacks. #R##N#In this paper, we taxonomize the space of model extraction attacks around two objectives: \emph{accuracy}, i.e., performing well on the underlying learning task, and \emph{fidelity}, i.e., matching the predictions of the remote victim classifier on any input. #R##N#To extract a high-accuracy model, we develop a learning-based attack which exploits the victim to supervise the training of an extracted model. #R##N#Through analytical and empirical arguments, we then explain the inherent limitations that prevent any learning-based strategy from extracting a truly high-fidelity model---i.e., extracting a functionally-equivalent model whose predictions are identical to those of the victim model on all possible inputs. Addressing these limitations, we expand on prior work to develop the first practical functionally-equivalent extraction attack for direct extraction (i.e., without training) of a model's weights. #R##N#We perform experiments both on academic datasets and a state-of-the-art image classifier trained with 1 billion proprietary images. In addition to broadening the scope of model extraction research, our work demonstrates the practicality of model extraction attacks against production-grade systems.

Connected through hyperedge HE-1 (induced by source paper "entangled watermarks as a defense against model extraction"):
  with members:
    - "similarity of neural network representations revisited"
    - "a framework for the extraction of deep neural networks by leveraging public data"
    - "energy and policy considerations for deep learning in nlp"
    - "detecting backdoor attacks on deep neural networks by activation clustering"
    - "adversarial risk and the dangers of evaluating against weak attacks"
    - "fine pruning defending against backdooring attacks on deep neural networks"
    - "knockoff nets stealing functionality of black box models"
    - "copycat cnn stealing knowledge by persuading confession with random non labeled data"

Connected through hyperedge HE-2 (induced by source paper "cryptanalytic extraction of neural network models"):
  with members:
    - "thieves on sesame street model extraction of bert based apis"
    - "analyzing and improving the image quality of stylegan"
    - "identifying weights and architectures of unknown relu networks"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "self training with noisy student improves imagenet classification"
    - "exploring connections between active learning and model extraction"
    - "manipulating machine learning poisoning attacks and countermeasures for regression learning"
    - "csi neural network using side channels to recover your artificial neural network information"

Connected through hyperedge HE-3 (induced by source paper "sniff reverse engineering of neural networks with fault attacks"):
  with members:
    - "terminal brain damage exposing the graceless degradation in deep neural networks under hardware fault attacks"
    - "a simple explanation for the existence of adversarial examples with small hamming distance"
    - "stealing hyperparameters in machine learning"
    - "learning transferable architectures for scalable image recognition"
    - "aggregated residual transformations for deep neural networks"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "densely connected convolutional networks"
    - "stealing machine learning models via prediction apis"

Connected through hyperedge HE-4 (induced by source paper "model extraction attacks against recurrent neural networks"):
  with members:
    - "a framework for the extraction of deep neural networks by leveraging public data"
    - "dawn dynamic adversarial watermarking of neural networks"
    - "extraction of complex dnn models real threat or boogeyman"
    - "knockoff nets stealing functionality of black box models"
    - "csi neural network using side channels to recover your artificial neural network information"
    - "prada protecting against dnn model stealing attacks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "stealing hyperparameters in machine learning"

Connected through hyperedge HE-5 (induced by source paper "prediction poisoning towards defenses against dnn model stealing attacks"):
  with members:
    - "a framework for the extraction of deep neural networks by leveraging public data"
    - "knockoff nets stealing functionality of black box models"
    - "copycat cnn stealing knowledge by persuading confession with random non labeled data"
    - "exploring connections between active learning and model extraction"
    - "stealing hyperparameters in machine learning"
    - "model extraction warning in mlaas paradigm"
    - "practical black box attacks against machine learning"
    - "transferability in machine learning from phenomena to black box attacks using adversarial samples"

Connected through hyperedge HE-6 (induced by source paper "span recovery for deep neural networks with applications to input obfuscation"):
  with members:
    - "learning two layer rectified neural networks in polynomial time"
    - "learning two layer neural networks with symmetric inputs"
    - "adversarial attacks on neural network policies"
    - "beating the perils of non convexity guaranteed training of neural networks using tensor methods"
    - "electron proton dynamics in deep learning"
    - "learning neural networks with two nonlinear layers in polynomial time"
    - "practical black box attacks against machine learning"
    - "reliably learning the relu in polynomial time"

Connected through hyperedge HE-7 (induced by source paper "mind your weight s a large scale study on insufficient machine learning model protection in mobile apps"):
  with members:
    - "on device neural net inference with mobile gpus"
    - "tensorscone a secure tensorflow framework using intel sgx"
    - "knockoff nets stealing functionality of black box models"
    - "turning your weakness into a strength watermarking deep neural networks by backdooring"
    - "csi neural network using side channels to recover your artificial neural network information"
    - "a first look at deep learning apps on smartphones"
    - "privado practical and secure dnn inference"
    - "stealing hyperparameters in machine learning"

Connected through hyperedge HE-8 (induced by source paper "deep neural network fingerprinting by conferrable adversarial examples"):
  with members:
    - "structure matters towards generating transferable adversarial images"
    - "on the robustness of the backdoor based watermarking in deep neural networks"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "ipguard protecting the intellectual property of deep neural networks via fingerprinting the classification boundary"
    - "how to prove your model belongs to you a blind watermark based framework to protect intellectual property of dnn"
    - "dawn dynamic adversarial watermarking of neural networks"
    - "on evaluating adversarial robustness"
    - "cinic 10 is not imagenet or cifar 10"

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)