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: "robust and effective malware detection through quantitative data flow graph metrics"
  Abstract: We present a novel malware detection approach based on metrics over quantitative data flow graphs. Quantitative data flow graphs (QDFGs) model process behavior by interpreting issued system calls as aggregations of quantifiable data flows.Due to the high abstraction level we consider QDFG metric based detection more robust against typical behavior obfuscation like bogus call injection or call reordering than other common behavioral models that base on raw system calls. We support this claim with experiments on obfuscated malware logs and demonstrate the superior obfuscation robustness in comparison to detection using n-grams. Our evaluations on a large and diverse data set consisting of about 7000 malware and 500 goodware samples show an average detection rate of 98.01% and a false positive rate of 0.48%. Moreover, we show that our approach is able to detect new malware (i.e. samples from malware families not included in the training set) and that the consideration of quantities in itself significantly improves detection precision.

Connected through hyperedge HE-1 (induced by source paper "towards learning representations of binary executable files for security tasks"):
  with members:
    - "graph matching networks for learning the similarity of graph structured objects"
    - "neural code comprehension a learnable representation of code semantics"
    - "vuldeepecker a deep learning based system for vulnerability detection"
    - "neural machine translation inspired binary code similarity comparison beyond function pairs"
    - "neural network based graph embedding for cross platform binary code similarity detection"
    - "on the naturalness of buggy code"

Connected through hyperedge HE-2 (induced by source paper "tools and techniques for malware detection and analysis"):
  with members:
    - "dl droid deep learning based android malware detection using real devices"
    - "mobile technology in healthcare environment security vulnerabilities and countermeasures"
    - "attacks and defenses in mobile ip modeling with stochastic game petri net"
    - "abusniff automatic detection and defenses against abusive facebook friends"
    - "deep neural network based malware detection using two dimensional binary program features"

Connected through hyperedge HE-3 (induced by source paper "survey of machine learning techniques for malware analysis"):
  with members:
    - "towards generic deobfuscation of windows api calls"
    - "a survey of symbolic execution techniques"
    - "back to the future malware detection with temporally consistent labels"
    - "novel feature extraction selection and fusion for effective malware family classification"
    - "deep neural network based malware detection using two dimensional binary program features"

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)