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: "towards poisoning of deep learning algorithms with back gradient optimization"
  Abstract: A number of online services nowadays rely upon machine learning to extract valuable information from data collected in the wild. This exposes learning algorithms to the threat of data poisoning, i.e., a coordinate attack in which a fraction of the training data is controlled by the attacker and manipulated to subvert the learning process. To date, these attacks have been devised only against a limited class of binary learning algorithms, due to the inherent complexity of the gradient-based procedure used to optimize the poisoning points (a.k.a. adversarial training examples). In this work, we rst extend the de nition of poisoning attacks to multiclass problems. We then propose a novel poisoning algorithm based on the idea of back-gradient optimization, i.e., to compute the gradient of interest through automatic di erentiation, while also reversing the learning procedure to drastically reduce the attack complexity. Compared to current poisoning strategies, our approach is able to target a wider class of learning algorithms, trained with gradient- based procedures, including neural networks and deep learning architectures. We empirically evaluate its e ectiveness on several application examples, including spam ltering, malware detection, and handwritten digit recognition. We nally show that, similarly to adversarial test examples, adversarial training examples can also be transferred across di erent learning algorithms.

Connected through hyperedge HE-1 (induced by source paper "certified robustness to label flipping attacks via randomized smoothing"):
  with members:
    - "tight certificates of adversarial robustness for randomly smoothed classifiers"
    - "on defending against label flipping attacks on malware detection systems"
    - "certified adversarial robustness via randomized smoothing"
    - "provably robust deep learning via adversarially trained smoothed classifiers"
    - "compressed sensing with adversarial sparse noise via l1 regression"
    - "learning with bad training data via iterative trimmed loss minimization"
    - "efficient algorithms and lower bounds for robust linear regression"
    - "representer point selection for explaining deep neural networks"

Connected through hyperedge HE-2 (induced by source paper "rab provable robustness against backdoor attacks"):
  with members:
    - "dynamic backdoor attacks against machine learning models"
    - "backdoor attacks against transfer learning with pre trained deep learning models"
    - "provable robust learning based on transformation specific smoothing"
    - "on certifying robustness against backdoor attacks via randomized smoothing"
    - "certified robustness to label flipping attacks via randomized smoothing"
    - "certified adversarial robustness via randomized smoothing"
    - "strip a defence against trojan attacks on deep neural networks"
    - "detecting backdoor attacks on deep neural networks by activation clustering"

Connected through hyperedge HE-3 (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"
    - "manipulating machine learning poisoning attacks and countermeasures for regression learning"
    - "is feature selection secure against training data poisoning"
    - "how to backdoor federated learning"

Connected through hyperedge HE-4 (induced by source paper "dataset distillation"):
  with members:
    - "network dissection quantifying interpretability of deep visual representations"
    - "few shot adversarial domain adaptation"
    - "data distillation towards omni supervised learning"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "data free knowledge distillation for deep neural networks"
    - "data poisoning attacks on factorization based collaborative filtering"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "understanding deep image representations by inverting them"

Connected through hyperedge HE-5 (induced by source paper "backdoor attacks against transfer learning with pre trained deep learning models"):
  with members:
    - "defending against misclassification attacks in transfer learning"
    - "fine pruning defending against backdooring attacks on deep neural networks"
    - "is feature selection secure against training data poisoning"
    - "potrojan powerful neural level trojan designs in deep learning models"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "generative poisoning attack method against neural networks"
    - "magnet a two pronged defense against adversarial examples"
    - "targeted backdoor attacks on deep learning systems using data poisoning"

Connected through hyperedge HE-6 (induced by source paper "defending against backdoor attack on deep neural networks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "topology attack and defense for graph neural networks an optimization perspective"
    - "req yolo a resource aware efficient quantization framework for object detection on fpgas"
    - "on the design of black box adversarial examples by leveraging gradient free optimization and operator splitting method"
    - "interpreting adversarial examples by activation promotion and suppression"
    - "detecting backdoor attacks on deep neural networks by activation clustering"
    - "fine pruning defending against backdooring attacks on deep neural networks"
    - "is feature selection secure against training data poisoning"

Connected through hyperedge HE-7 (induced by source paper "investigating generalization in neural networks under optimally evolved training perturbations"):
  with members:
    - "adaptive gradient methods with dynamic bound of learning rate"
    - "built in vulnerabilities to imperceptible adversarial perturbations"
    - "excessive invariance causes adversarial vulnerability"
    - "recurrent world models facilitate policy evolution"
    - "certified defenses for data poisoning attacks"
    - "sharp minima can generalize for deep nets"
    - "improving generalization performance by switching from adam to sgd"
    - "understanding deep learning requires rethinking generalization"

Connected through hyperedge HE-8 (induced by source paper "can t boil this frog robustness of online trained autoencoder based anomaly detectors to adversarial poisoning attacks"):
  with members:
    - "efficient cyber attacks detection in industrial control systems using lightweight neural networks"
    - "real time evasion attacks with physical constraints on deep learning based anomaly detectors in industrial control systems"
    - "deep learning for anomaly detection a survey"
    - "intrusion detection for industrial control systems evaluation analysis and adversarial attacks"
    - "manipulating machine learning poisoning attacks and countermeasures for regression learning"
    - "poison frogs targeted clean label poisoning attacks on neural networks"
    - "adversarial regression for detecting attacks in cyber physical systems"
    - "a deep learning based framework for conducting stealthy attacks in industrial control 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.LG(Machine Learning)