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: "defending against misclassification attacks in transfer learning"
  Abstract: Transfer learning accelerates the development of new models (Student Models). It applies relevant knowledge from a pre-trained model (Teacher Model) to the new ones with a small amount of training data, yet without affecting the model accuracy. However, these Teacher Models are normally open in order to facilitate sharing and reuse, which creates an attack plane in transfer learning systems. Among others, recent emerging attacks demonstrate that adversarial inputs can be built with negligible perturbations to the normal inputs. Such inputs can mimic the internal features of the student models directly based on the knowledge of the Teacher Models and cause misclassification in final predictions. #R##N#In this paper, we propose an effective defence against the above misclassification attacks in transfer learning. First, we propose a distilled differentiator that can address the targeted attacks, where adversarial inputs are misclassified to a specific class. Specifically, this dedicated differentiator is designed with network activation pruning and retraining in a fine-tuned manner, so as to reach high defence rates and high model accuracy. To address the non-targeted attacks that misclassify adversarial inputs to randomly selected classes, we further employ an ensemble structure from the differentiators to cover all possible misclassification. Our evaluations over common image recognition tasks confirm that the student models applying our defence can reject most of the adversarial inputs with a marginal accuracy loss. We also show that our defence outperforms prior approaches in both targeted and non-targeted attacks.

Connected through hyperedge HE-1 (induced by source paper "backdoor attacks against transfer learning with pre trained deep learning models"):
  with members:
    - "fine pruning defending against backdooring attacks on deep neural networks"
    - "is feature selection secure against training data poisoning"
    - "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"

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)