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: "a target agnostic attack on deep models exploiting security vulnerabilities of transfer learning"
  Abstract: Due to the lack of enough training data and high computational cost to train a deep neural network from scratch, transfer learning has been extensively used in many deep-neural-network-based applications, such as face recognition, image classification, speech recognition, etc. A commonly-used transfer learning approach involves taking a part of a pre-trained model, adding a few layers at the end, and re-training the new layers with a small dataset. This approach, while efficient and widely used, imposes a security vulnerability because the pre-trained models used in transfer learning are usually available publicly to everyone, including potential attackers. In this paper, we show that without any additional knowledge other than the pre-trained model, an attacker can launch an effective and efficient brute force attack that can craft instances of input to trigger each target class with high confidence. Note that we assume that the attacker does not have access to any target-specific information, including samples from target classes, re-trained model, and probabilities assigned by Softmax to each class, and thus called target-agnostic attack. These assumptions render all previous attacks impractical, to the best of our knowledge. To evaluate the proposed attack, we perform a set of experiments on face recognition and speech recognition tasks and show the effectiveness of the attack. Our work sheds light on a fundamental security challenge of transfer learning in deep neural networks.

Connected through hyperedge HE-1 (induced by source paper "improving the adversarial robustness of transfer learning via noisy feature distillation"):
  with members:
    - "single path nas designing hardware efficient convnets in less than 4 hours"
    - "growing a brain fine tuning by increasing model capacity"
    - "legr filter pruning via learned global ranking"
    - "transfusion understanding transfer learning for medical imaging"
    - "adversarially robust transfer learning"
    - "advertorch v0 1 an adversarial robustness toolbox based on pytorch"
    - "mixout effective regularization to finetune large scale pretrained language models"
    - "rethinking imagenet pre training"

Connected through hyperedge HE-2 (induced by source paper "hidden trigger backdoor attacks"):
  with members:
    - "strip a defence against trojan attacks on deep neural networks"
    - "gotta catch em all using concealed trapdoors to detect adversarial attacks on neural networks"
    - "poisoning attacks with generative adversarial nets"
    - "using honeypots to catch adversarial attacks on neural networks"
    - "detecting backdoor attacks on deep neural networks by activation clustering"
    - "fine pruning defending against backdooring attacks on deep neural networks"
    - "backdoor embedding in convolutional neural network models via invisible perturbation"
    - "spectral signatures in backdoor attacks"

Connected through hyperedge HE-3 (induced by source paper "large scale mobile app identification using deep learning"):
  with members:
    - "multitask learning for network traffic classification"
    - "how to achieve high classification accuracy with just a few labels a semi supervised approach using sampled packets"
    - "overcoming catastrophic forgetting problem by weight consolidation and long term memory"
    - "deep learning for encrypted traffic classification an overview"
    - "the dark side channel of mobile devices a survey on network traffic analysis"
    - "robust smartphone app identification via encrypted network traffic analysis"
    - "can t you hear me knocking identification of user actions on android apps via traffic analysis"
    - "visualizing and understanding convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "security of deep learning methodologies challenges and opportunities"):
  with members:
    - "model reuse attacks on deep learning systems"
    - "to compress or not to compress understanding the interactions between adversarial attacks and neural network compression"
    - "byzantine robust distributed learning towards optimal statistical rates"
    - "how to backdoor federated learning"
    - "understanding membership inferences on well generalized learning models"
    - "differentially private federated learning a client level perspective"
    - "deep models under the gan information leakage from collaborative deep learning"

Connected through hyperedge HE-5 (induced by source paper "multitask learning for network traffic classification"):
  with members:
    - "how to achieve high classification accuracy with just a few labels a semi supervised approach using sampled packets"
    - "overcoming catastrophic forgetting problem by weight consolidation and long term memory"
    - "deep learning for encrypted traffic classification an overview"

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)