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: "digital investigation of pdf files unveiling traces of embedded malware"
  Abstract: Over the last decade, malicious software (or malware, for short) has shown an increasing sophistication and proliferation, fueled by a flourishing underground economy, in response to the increasing complexity of modern defense mechanisms. PDF documents are among the major vectors used to convey malware, thanks to the flexibility of their structure and the ability of embedding different kinds of content, ranging from images to JavaScript code. Despite the numerous efforts made by the research and industrial communities, PDF malware is still one of the major threats on the cyber-security landscape. In this paper, we provide an overview of the current attack techniques used to convey PDF malware, and discuss state-of-the-art PDF malware analysis tools that provide valuable support to digital forensic investigations. We finally discuss limitations and open issues of the current defense mechanisms, and sketch some interesting future research directions.

Connected through hyperedge HE-1 (induced by source paper "towards adversarial malware detection lessons learned from pdf based attacks"):
  with members:
    - "explaining vulnerabilities of deep learning to adversarial malware binaries"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "model reuse attacks on deep learning systems"
    - "adversarial malware binaries evading deep learning for malware detection in executables"
    - "manipulating machine learning poisoning attacks and countermeasures for regression learning"
    - "explaining black box android malware detection"
    - "why do adversarial attacks transfer explaining transferability of evasion and poisoning attacks"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"

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)