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: "deep transfer learning for static malware classification"
  Abstract: We propose to apply deep transfer learning from computer vision to static malware classification. In the transfer learning scheme, we borrow knowledge from natural images or objects and apply to the target domain of static malware detection. As a result, training time of deep neural networks is accelerated while high classification performance is still maintained. We demonstrate the effectiveness of our approach on three experiments and show that our proposed method outperforms other classical machine learning methods measured in accuracy, false positive rate, true positive rate and $F_1$ score (in binary classification). We instrument an interpretation component to the algorithm and provide interpretable explanations to enhance security practitioners' trust to the model. We further discuss a convex combination scheme of transfer learning and training from scratch for enhanced malware detection, and provide insights of the algorithmic interpretation of vision-based malware classification techniques.

Connected through hyperedge HE-1 (induced by source paper "random capsnet forest model for imbalanced malware type classification task"):
  with members:
    - "convolutional capsule network for classification of breast cancer histology images"
    - "deceiving end to end deep learning malware detectors using adversarial examples"
    - "capsacc an efficient hardware accelerator for capsulenets with data reuse"
    - "fast capsnet for lung cancer screening"
    - "brain tumor type classification via capsule networks"
    - "microsoft malware classification challenge"
    - "deep learning at the shallow end malware classification for non domain experts"
    - "capsule networks against medical imaging data challenges"

Connected through hyperedge HE-2 (induced by source paper "to believe or not to believe validating explanation fidelity for dynamic malware analysis"):
  with members:
    - "henet a deep learning approach on intel circledr processor trace for effective exploit detection"
    - "imbalanced malware images classification a cnn based approach"
    - "a unified approach to interpreting model predictions"
    - "interpretable explanations of black boxes by meaningful perturbation"
    - "why should i trust you explaining the predictions of any classifier"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-3 (induced by source paper "understanding the efficacy reliability and resiliency of computer vision techniques for malware detection and future research directions"):
  with members:
    - "towards resilient machine learning for ransomware detection"
    - "microsoft malware classification challenge"
    - "henet a deep learning approach on intel circledr processor trace for effective exploit detection"
    - "imbalanced malware images classification a cnn based approach"
    - "why should i trust you explaining the predictions of any classifier"

Connected through hyperedge HE-4 (induced by source paper "dynamic data fusion using multi input models for malware classification"):
  with members:
    - "microsoft malware classification challenge"
    - "comparison of deep learning and the classical machine learning algorithm for the malware detection"

Connected through hyperedge HE-5 (induced by source paper "towards resilient machine learning for ransomware detection"):
  with members:
    - "deep learning for malicious flow detection"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "detecting adversarial examples via neural fingerprinting"
    - "leveraging machine learning techniques for windows ransomware network traffic detection"
    - "adversarial logit pairing"
    - "generating adversarial examples with adversarial networks"
    - "black box adversarial attacks with limited queries and information"
    - "shield fast practical defense and vaccination for deep learning using jpeg compression"

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)