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: "stealing hyperparameters in machine learning"
  Abstract: Hyperparameters are critical in machine learning, as different hyperparameters often result in models with significantly different performance. Hyperparameters may be deemed confidential because of their commercial value and the confidentiality of the proprietary algorithms that the learner uses to learn them. In this work, we propose attacks on stealing the hyperparameters that are learned by a learner. We call our attacks hyperparameter stealing attacks. Our attacks are applicable to a variety of popular machine learning algorithms such as ridge regression, logistic regression, support vector machine, and neural network. We evaluate the effectiveness of our attacks both theoretically and empirically. For instance, we evaluate our attacks on Amazon Machine Learning. Our results demonstrate that our attacks can accurately steal hyperparameters. We also study countermeasures. Our results highlight the need for new defenses against our hyperparameter stealing attacks for certain machine learning algorithms.

Connected through hyperedge HE-1 (induced by source paper "dynamic backdoor attacks against machine learning models"):
  with members:
    - "memguard defending against black box membership inference attacks via adversarial examples"
    - "strip a defence against trojan attacks on deep neural networks"
    - "under the hood of membership inference attacks on aggregate location time series"
    - "updates leak data set inference and reconstruction attacks in online learning"
    - "exploiting unintended feature leakage in collaborative learning"
    - "knockoff nets stealing functionality of black box models"
    - "tagvisor a privacy advisor for sharing hashtags"
    - "the natural auditor how to tell if someone used your words to train their model"

Connected through hyperedge HE-2 (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-3 (induced by source paper "influence function based data poisoning attacks to top n recommender systems"):
  with members:
    - "private and communication efficient edge learning a sparse differential gaussian masking distributed sgd approach"
    - "byzantine resilient stochastic gradient descent for distributed learning a lipschitz inspired coordinate wise median approach"
    - "on the accuracy of influence functions for measuring group effects"
    - "local model poisoning attacks to byzantine robust federated learning"
    - "attacking graph based classification via manipulating the graph structure"
    - "poisoning attacks to graph based recommender systems"
    - "data dropout optimizing training data for convolutional neural networks"
    - "byzantine robust distributed learning towards optimal statistical rates"

Connected through hyperedge HE-4 (induced by source paper "sniff reverse engineering of neural networks with fault attacks"):
  with members:
    - "high fidelity extraction of neural network models"
    - "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"
    - "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-5 (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"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "prediction poisoning towards defenses against dnn model stealing attacks"):
  with members:
    - "high fidelity extraction of neural network models"
    - "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"
    - "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-7 (induced by source paper "ensei efficient secure inference via frequency domain homomorphic convolution for privacy preserving visual recognition"):
  with members:
    - "privacy preserving image based localization"
    - "xonn xnor based oblivious deep neural network inference"
    - "towards privacy preserving visual recognition via adversarial training a pilot study"
    - "low latency privacy preserving inference"
    - "gazelle a low latency framework for secure neural network inference"
    - "faster cryptonets leveraging sparsity for real world encrypted inference"
    - "deepsecure scalable provably secure deep learning"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"

Connected through hyperedge HE-8 (induced by source paper "mitigating query flooding parameter duplication attack on regression models with high dimensional gaussian mechanism"):
  with members:
    - "mvg mechanism differential privacy under matrix valued query"
    - "stealing neural networks via timing side channels"
    - "prada protecting against dnn model stealing attacks"
    - "utility optimized local differential privacy mechanisms for distribution estimation"
    - "model extraction warning in mlaas paradigm"
    - "individual differential privacy a utility preserving formulation of differential privacy guarantees"
    - "practical black box attacks against machine learning"
    - "stealing machine learning models via prediction apis"

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)