human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "a backdoor attack against lstm based text classification systems"):
  Abstract: With the widespread use of deep learning system in many applications, the adversary has strong incentive to explore vulnerabilities of deep neural networks and manipulate them. Backdoor attacks against deep neural networks have been reported to be a new type of threat. In this attack, the adversary will inject backdoors into the model and then cause the misbehavior of the model through inputs including backdoor triggers. Existed research mainly focuses on backdoor attacks in image classification based on CNN, little attention has been paid to the backdoor attacks in RNN. In this paper, we implement a backdoor attack in text classification based on LSTM by data poisoning. When the backdoor is injected, the model will misclassify any text samples that contains a specific trigger sentence into the target category determined by the adversary. The existence of the backdoor trigger is stealthy and the backdoor injected has little impact on the performance of the model. We consider the backdoor attack in black-box setting where the adversary has no knowledge of model structures or training algorithms except for small amount of training data. We verify the attack through sentiment analysis on the dataset of IMDB movie reviews. The experimental results indicate that our attack can achieve around 95% success rate with 1% poisoning rate.

Includes members:
  Member "design of intentional backdoors in sequential models":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the trojai software framework an opensource tool for embedding trojans into deep learning models")
  Member "detecting backdoor attacks on deep neural networks by activation clustering":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "vulnerabilities of connectionist ai applications evaluation and defence")
  Member "spectral signatures in backdoor attacks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "vulnerabilities of connectionist ai applications evaluation and defence")
  Member "how to backdoor federated learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "trends and advancements in deep neural network communication")
  Member "is data clustering in adversarial settings secure":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "adversarial learning in statistical classification a comprehensive review of defenses against attacks")
  Member "generative poisoning attack method against neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "humpty dumpty controlling word meanings via corpus poisoning")
  Member "badnets identifying vulnerabilities in the machine learning model supply chain":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "vulnerabilities of connectionist ai applications evaluation and defence")
  Member "targeted backdoor attacks on deep learning systems using data poisoning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "vulnerabilities of connectionist ai applications evaluation and defence")

Question: Please tell me which class the source paper 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)