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: "is feature selection secure against training data poisoning"
  Abstract: Learning in adversarial settings is becoming an important task for application domains where attackers may inject malicious data into the training set to subvert normal operation of data-driven technologies. Feature selection has been widely used in machine learning for security applications to improve generalization and computational efficiency, although it is not clear whether its use may be beneficial or even counterproductive when training data are poisoned by intelligent attackers. In this work, we shed light on this issue by providing a framework to investigate the robustness of popular feature selection methods, including LASSO, ridge regression and the elastic net. Our results on malware detection show that feature selection methods can be significantly compromised under attack (we can reduce LASSO to almost random choices of feature sets by careful insertion of less than 5% poisoned training samples), highlighting the need for specific countermeasures.

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 "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"
    - "how to backdoor federated learning"
    - "ml leaks model and data independent membership inference attacks and defenses on machine learning models"

Connected through hyperedge HE-3 (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"
    - "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"
    - "towards poisoning of deep learning algorithms with back gradient optimization"
    - "targeted backdoor attacks on deep learning systems using data poisoning"

Connected through hyperedge HE-4 (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-5 (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"
    - "spectral signatures in backdoor attacks"

Connected through hyperedge HE-6 (induced by source paper "action manipulation attacks against stochastic bandits attacks and defense"):
  with members:
    - "robust stochastic bandit algorithms under probabilistic unbounded adversarial attack"
    - "data poisoning attacks on stochastic bandits"
    - "the intrinsic robustness of stochastic bandits to strategic manipulation"
    - "stochastic bandits robust to adversarial corruptions"
    - "data poisoning attacks in contextual bandits"
    - "adversarial attacks on neural network policies"
    - "tactics of adversarial attack on deep reinforcement learning agents"
    - "data poisoning attacks on factorization based collaborative filtering"

Connected through hyperedge HE-7 (induced by source paper "regularisation can mitigate poisoning attacks a novel analysis based on multiobjective bilevel optimisation"):
  with members:
    - "poisoning attacks with generative adversarial nets"
    - "sever a robust meta algorithm for stochastic optimization"
    - "poison frogs targeted clean label poisoning attacks on neural networks"
    - "certified defenses for data poisoning attacks"
    - "towards poisoning of deep learning algorithms with back gradient optimization"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "automated crowdturfing attacks and defenses in online review systems"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-8 (induced by source paper "adversarial examples for deep learning cyber security analytics"):
  with members:
    - "robust audio adversarial example for a physical attack"
    - "certified adversarial robustness via randomized smoothing"
    - "on designing machine learning models for malicious network traffic classification"
    - "rallying adversarial techniques against deep learning for network security"
    - "evading classifiers in discrete domains with provable optimality guarantees"
    - "kitsune an ensemble of autoencoders for online network intrusion detection"
    - "black box generation of adversarial text sequences to evade deep learning classifiers"
    - "deceiving end to end deep learning malware detectors using adversarial examples"

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)