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: "manipulating machine learning poisoning attacks and countermeasures for regression learning"
  Abstract: As machine learning becomes widely used for automated decisions, attackers have strong incentives to manipulate the results and models generated by machine learning algorithms. In this paper, we perform the first systematic study of poisoning attacks and their countermeasures for linear regression models. In poisoning attacks, attackers deliberately influence the training data to manipulate the results of a predictive model. We propose a theoretically-grounded optimization framework specifically designed for linear regression and demonstrate its effectiveness on a range of datasets and models. We also introduce a fast statistical attack that requires limited knowledge of the training process. Finally, we design a new principled defense method that is highly resilient against all poisoning attacks. We provide formal guarantees about its convergence and an upper bound on the effect of poisoning attacks when the defense is deployed. We evaluate extensively our attacks and defenses on three realistic datasets from health care, loan assessment, and real estate domains.

Connected through hyperedge HE-1 (induced by source paper "secure and robust machine learning for healthcare a survey"):
  with members:
    - "securing future autonomous connected vehicles challenges posed by adversarial machine learning and the way forward"
    - "reproducibility in machine learning for health"
    - "fairness in algorithmic decision making an excursion through the lens of causality"
    - "caveat emptor the risks of using big data for human development"
    - "motion corrected multishot mri reconstruction using generative networks with sensitivity encoding"
    - "the adversarial machine learning conundrum can the insecurity of ml become the achilles heel of cognitive networks"
    - "self supervised domain adaptation for computer vision tasks"
    - "feature robustness in non stationary health records caveats to deployable model performance in common clinical machine learning tasks"

Connected through hyperedge HE-2 (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-3 (induced by source paper "entangled watermarks as a defense against model extraction"):
  with members:
    - "similarity of neural network representations revisited"
    - "high fidelity extraction of neural network models"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "on the effectiveness of mitigating data poisoning attacks with gradient shaping"):
  with members:
    - "data poisoning against differentially private learners attacks and defenses"
    - "evaluating differentially private machine learning in practice"
    - "analyzing federated learning through an adversarial lens"
    - "byzantine robust distributed learning towards optimal statistical rates"
    - "sever a robust meta algorithm for stochastic optimization"
    - "is feature selection secure against training data poisoning"
    - "how to backdoor federated learning"
    - "ml leaks model and data independent membership inference attacks and defenses on machine learning models"

Connected through hyperedge HE-5 (induced by source paper "adversarial machine learning industry perspectives"):
  with members:
    - "the deepfake detection challenge dfdc preview dataset"
    - "secml a python library for secure and explainable machine learning"
    - "failure modes in machine learning systems"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "adversarial music real world audio adversary against wake word detection system"
    - "adversarial generative nets neural network attacks on state of the art face recognition"
    - "adversarial reprogramming of neural networks"
    - "a marauder s map of security and privacy in machine learning"

Connected through hyperedge HE-6 (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"
    - "csi neural network using side channels to recover your artificial neural network information"

Connected through hyperedge HE-7 (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-8 (induced by source paper "adversarial attacks on linear contextual bandits"):
  with members:
    - "stochastic linear optimization with adversarial corruption"
    - "data poisoning attacks on stochastic bandits"
    - "corruption robust exploration in episodic reinforcement learning"
    - "better algorithms for stochastic bandits with adversarial corruptions"
    - "targeted attacks on deep reinforcement learning agents through adversarial observations"
    - "data poisoning attacks in contextual bandits"
    - "adversarial bandits with knapsacks"
    - "a comparative study of matrix factorization and random walk with restart in recommender systems"

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)