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: "detecting backdoor attacks on deep neural networks by activation clustering"
  Abstract: While machine learning (ML) models are being increasingly trusted to make decisions in different and varying areas, the safety of systems using such models has become an increasing concern. In particular, ML models are often trained on data from potentially untrustworthy sources, providing adversaries with the opportunity to manipulate them by inserting carefully crafted samples into the training set. Recent work has shown that this type of attack, called a poisoning attack, allows adversaries to insert backdoors or trojans into the model, enabling malicious behavior with simple external backdoor triggers at inference time and only a blackbox perspective of the model itself. Detecting this type of attack is challenging because the unexpected behavior occurs only when a backdoor trigger, which is known only to the adversary, is present. Model users, either direct users of training data or users of pre-trained model from a catalog, may not guarantee the safe operation of their ML-based system. In this paper, we propose a novel approach to backdoor detection and removal for neural networks. Through extensive experimental results, we demonstrate its effectiveness for neural networks classifying text and images. To the best of our knowledge, this is the first methodology capable of detecting poisonous data crafted to insert backdoors and repairing the model that does not require a verified and trusted dataset.

Connected through hyperedge HE-1 (induced by source paper "vulnerabilities of connectionist ai applications evaluation and defence"):
  with members:
    - "towards ground truth evaluation of visual explanations"
    - "continual lifelong learning with neural networks a review"
    - "programmable neural network trojan for pre trained feature extractor"
    - "hidden trigger backdoor attacks"
    - "adversarial attacks and defenses in images graphs and text a review"
    - "on evaluating adversarial robustness"
    - "explainable ai for trees from local explanations to global understanding"
    - "wild patterns ten years after the rise of adversarial machine learning"

Connected through hyperedge HE-2 (induced by source paper "fawkes protecting personal privacy against unauthorized deep learning models"):
  with members:
    - "strong baseline defenses against clean label poisoning attacks"
    - "anonymousnet natural face de identification with measurable privacy"
    - "hiding faces in plain sight disrupting ai face synthesis with adversarial perturbations"
    - "fooling automated surveillance cameras adversarial patches to attack person detection"
    - "making an invisibility cloak real world adversarial attacks on object detectors"
    - "advhat real world adversarial attack on arcface face id system"
    - "using honeypots to catch adversarial attacks on neural networks"
    - "privacy protective gan for face de identification"

Connected through hyperedge HE-3 (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"
    - "sentinet detecting physical attacks against deep learning systems"

Connected through hyperedge HE-4 (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"
    - "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"
    - "copycat cnn stealing knowledge by persuading confession with random non labeled data"

Connected through hyperedge HE-5 (induced by source paper "februus input purification defense against trojan attacks on deep neural network systems"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "strip a defence against trojan attacks on deep neural networks"
    - "tabor a highly accurate approach to inspecting and restoring trojan backdoors in ai systems"
    - "fine pruning defending against backdooring attacks on deep neural networks"
    - "sentinet detecting physical attacks against deep learning systems"
    - "hu fu hardware and software collaborative attack framework against neural networks"
    - "how to backdoor federated learning"
    - "hardware trojan attacks on neural networks"

Connected through hyperedge HE-6 (induced by source paper "towards probabilistic verification of machine unlearning"):
  with members:
    - "formalizing data deletion in the context of the right to be forgotten"
    - "nnoculation broad spectrum and targeted treatment of backdoored dnns"
    - "machine unlearning linear filtration for logit based classifiers"
    - "bypassing backdoor detection algorithms in deep learning"
    - "hidden trigger backdoor attacks"
    - "making ai forget you data deletion in machine learning"
    - "certified data removal from machine learning models"
    - "auditing data provenance in text generation models"

Connected through hyperedge HE-7 (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"
    - "fine pruning defending against backdooring attacks on deep neural networks"
    - "is feature selection secure against training data poisoning"
    - "spectral signatures in backdoor attacks"

Connected through hyperedge HE-8 (induced by source paper "on certifying robustness against backdoor attacks via randomized smoothing"):
  with members:
    - "macer attack free and scalable robust training via maximizing certified radius"
    - "certified robustness of community detection against adversarial structural perturbation via randomized smoothing"
    - "tight certificates of adversarial robustness for randomly smoothed classifiers"
    - "certified adversarial robustness via randomized smoothing"
    - "strip a defence against trojan attacks on deep neural networks"
    - "defending neural backdoors via generative distribution modeling"
    - "certified robustness for top k predictions against adversarial perturbations via randomized smoothing"
    - "tabor a highly accurate approach to inspecting and restoring trojan backdoors in ai 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)