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: "bypassing backdoor detection algorithms in deep learning"
  Abstract: Deep learning models are known to be vulnerable to various adversarial manipulations of the training data, model parameters, and input data. In particular, an adversary can modify the training data and model parameters to embed backdoors into the model, so the model behaves according to the adversary's objective if the input contains the backdoor features (e.g., a stamp on an image). The poisoned model's behavior on clean data, however, remains unchanged. Many detection algorithms are designed to detect backdoors on input samples or model activation functions, in order to remove the backdoor. These algorithms rely on the statistical difference between the latent representations of backdoor-enabled and clean input data in the poisoned model. In this paper, we design an adversarial backdoor embedding algorithm that can bypass the existing detection algorithms including the state-of-the-art techniques (published in IEEE S\&P 2019 and NeurIPS 2018). We design a strategic adversarial training that optimizes the original loss function of the model, and also maximizes the indistinguishability of the hidden representations of poisoned data and clean data. We show the effectiveness of our attack on multiple datasets and model architectures. This work calls for designing adversary-aware defense mechanisms for backdoor detection algorithms.

Connected through hyperedge HE-1 (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"
    - "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"
    - "detecting backdoor attacks on deep neural networks by activation clustering"

Connected through hyperedge HE-2 (induced by source paper "backdooring and poisoning neural networks with image scaling attacks"):
  with members:
    - "misleading authorship attribution of source code using adversarial learning"
    - "poison frogs targeted clean label poisoning attacks on neural networks"
    - "badnets identifying vulnerabilities in the machine learning model supply chain"
    - "targeted backdoor attacks on deep learning systems using data poisoning"
    - "stealing machine learning models via prediction apis"
    - "membership inference attacks against machine learning models"
    - "towards evaluating the robustness of neural networks"
    - "rethinking the inception architecture for computer vision"

Connected through hyperedge HE-3 (induced by source paper "the trojai software framework an opensource tool for embedding trojans into deep learning models"):
  with members:
    - "design of intentional backdoors in sequential models"
    - "trojdrl trojan attacks on deep reinforcement learning agents"
    - "making an invisibility cloak real world adversarial attacks on object detectors"
    - "potrojan powerful neural level trojan designs in deep learning models"
    - "badnets identifying vulnerabilities in the machine learning model supply chain"
    - "adam a method for stochastic optimization"

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)