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: "csi neural network using side channels to recover your artificial neural network information"
  Abstract: Machine learning has become mainstream across industries. Numerous examples proved the validity of it for security applications. In this work, we investigate how to reverse engineer a neural network by using only power side-channel information. To this end, we consider a multilayer perceptron as the machine learning architecture of choice and assume a non-invasive and eavesdropping attacker capable of measuring only passive side-channel leakages like power consumption, electromagnetic radiation, and reaction time. #R##N#We conduct all experiments on real data and common neural net architectures in order to properly assess the applicability and extendability of those attacks. Practical results are shown on an ARM CORTEX-M3 microcontroller. Our experiments show that the side-channel attacker is capable of obtaining the following information: the activation functions used in the architecture, the number of layers and neurons in the layers, the number of output classes, and weights in the neural network. Thus, the attacker can effectively reverse engineer the network using side-channel information. #R##N#Next, we show that once the attacker has the knowledge about the neural network architecture, he/she could also recover the inputs to the network with only a single-shot measurement. Finally, we discuss several mitigations one could use to thwart such attacks.

Connected through hyperedge HE-1 (induced by source paper "cryptanalytic extraction of neural network models"):
  with members:
    - "high fidelity extraction of neural network models"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "model extraction attacks against recurrent neural networks"):
  with members:
    - "high fidelity extraction of neural network models"
    - "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"
    - "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-3 (induced by source paper "mind your weight s a large scale study on insufficient machine learning model protection in mobile apps"):
  with members:
    - "high fidelity extraction of neural network models"
    - "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"
    - "a first look at deep learning apps on smartphones"
    - "privado practical and secure dnn inference"
    - "stealing hyperparameters in machine learning"

Connected through hyperedge HE-4 (induced by source paper "survey of attacks and defenses on edge deployed neural networks"):
  with members:
    - "tensorflow js machine learning for the web and beyond"
    - "bit flip attack crushing neural network with progressive bit search"
    - "xonn xnor based oblivious deep neural network inference"
    - "deeplaser practical fault attack on deep neural networks"
    - "knockoff nets stealing functionality of black box models"
    - "cache telepathy leveraging shared resource attacks to learn dnn architectures"
    - "mlcapsule guarded offline deployment of machine learning as a service"
    - "the secret sharer measuring unintended neural network memorization extracting secrets"

Connected through hyperedge HE-5 (induced by source paper "high accuracy and high fidelity extraction of neural networks"):
  with members:
    - "s4l self supervised semi supervised learning"
    - "zero shot knowledge transfer via adversarial belief matching"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "high fidelity extraction of neural network models"):
  with members:
    - "s4l self supervised semi supervised learning"
    - "zero shot knowledge transfer via adversarial belief matching"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "open dnn box by power side channel attack"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "finefool fine object contour attack via attention"
    - "i know what you see power side channel attack on convolutional neural network accelerators"
    - "stealing neural networks via timing side channels"
    - "accelerator aware pruning for convolutional neural networks"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "nisp pruning networks using neuron importance score propagation"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-8 (induced by source paper "quantifying hyper parameter leakage in machine learning"):
  with members:
    - "prediction poisoning towards defenses against dnn model stealing attacks"
    - "a tutorial on learning with bayesian networks"
    - "a framework for the extraction of deep neural networks by leveraging public data"
    - "neural network model extraction attacks in edge devices by hearing architectural hints"
    - "f bleau fast black box leakage estimation"
    - "knockoff nets stealing functionality of black box models"
    - "cache telepathy leveraging shared resource attacks to learn dnn architectures"
    - "exploring connections between active learning and model extraction"

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.CR(Cryptography and Security)