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 learning at the shallow end malware classification for non domain experts"
  Abstract: Abstract   Current malware detection and classification approaches generally rely on time consuming and knowledge intensive processes to extract patterns (signatures) and behaviors from malware, which are then used for identification. Moreover, these signatures are often limited to local, contiguous sequences within the data whilst ignoring their context in relation to each other and throughout the malware file as a whole. We present a Deep Learning based malware classification approach that requires no expert domain knowledge and is based on a purely data driven approach for complex pattern and feature identification.

Connected through hyperedge HE-1 (induced by source paper "feature level malware obfuscation in deep learning"):
  with members:
    - "adversarial attacks and defenses in images graphs and text a review"
    - "effectiveness of adversarial examples and defenses for malware classification"
    - "a large scale investigation of obfuscation use in google play"
    - "adversarial malware binaries evading deep learning for malware detection in executables"
    - "exploring adversarial examples in malware detection"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "motivating the rules of the game for adversarial example research"
    - "adversarial perturbations against deep neural networks for malware classification"

Connected through hyperedge HE-2 (induced by source paper "scratch that an evolution based adversarial attack against neural networks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "one pixel attack for fooling deep neural networks"
    - "they might not be giants crafting black box adversarial examples with fewer queries using particle swarm optimization"
    - "adversarial camera stickers a physical camera based attack on deep learning systems"
    - "adversarial patch"
    - "generating natural language adversarial examples"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "genattack practical black box attacks with gradient free optimization"

Connected through hyperedge HE-3 (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"
    - "deep transfer learning for static malware classification"
    - "brain tumor type classification via capsule networks"
    - "microsoft malware classification challenge"
    - "capsule networks against medical imaging data challenges"

Connected through hyperedge HE-4 (induced by source paper "methodology for the automated metadata based classification of incriminating digital forensic artefacts"):
  with members:
    - "increasing digital investigator availability through efficient workflow management and automation"
    - "evaluation of digital forensic process models with respect to digital forensics as a service"
    - "battling the digital forensic backlog through data deduplication"
    - "current challenges and future research areas for digital forensic investigation"
    - "tiered forensic methodology model for digital field triage by non digital evidence specialists"

Connected through hyperedge HE-5 (induced by source paper "improving borderline adulthood facial age estimation through ensemble learning"):
  with members:
    - "age progression regression by conditional adversarial autoencoder"
    - "current challenges and future research areas for digital forensic investigation"
    - "yfcc100m the new data in multimedia research"

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.CR(Cryptography and Security)