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: "virtual machine introspection based malware behavior profiling and family grouping"
  Abstract: The proliferation of malwares have been attributed to the alternations of a handful of original malware source codes. The malwares alternated from the same origin share some intrinsic behaviors and form a malware family. Expediently, identifying its malware family when a malware is first seen on the Internet can provide useful clues to mitigate the threat. In this paper, a malware profiler (VMP) is proposed to profile the execution behaviors of a malware by leveraging virtual machine introspection (VMI) technique. The VMP inserts plug-ins inside the virtual machine monitor (VMM) to record the invoked API calls with their input parameters and return values as the profile of malware. In this paper, a popular similarity measurement Jaccard distance and a phylogenetic tree construction method are adopted to discover malware families. The studies of malware profiles show the malwares from a malware family are very similar to each others and distinct from other malware families as well as benign software. This paper also examines VMP against existing anti-malware detection engines and some well-known malware grouping methods to compare the goodness in their malware family constructions. A peer voting approach is proposed and the results show VMP is better than almost all of the compared anti-malware engines, and compatible with the fine tuned text-mining approach and high order N-gram approaches. We also establish a malware profiling website based on VMP for malware research.

Connected through hyperedge HE-1 (induced by source paper "integration of static and dynamic analysis for malware family classification with composite neural network"):
  with members:
    - "theoretical investigation of composite neural network"
    - "behavioral malware classification using convolutional recurrent neural networks"
    - "a family of droids android malware detection via behavioral modeling static vs dynamic analysis"
    - "robust neural malware detection models for emulation sequence learning"
    - "attack and defense of dynamic analysis based adversarial neural malware classification models"
    - "attention is all you need"
    - "dynamic analysis of executables to detect and characterize malware"
    - "distributed representations of sentences and documents"

Connected through hyperedge HE-2 (induced by source paper "learning malware representation based on execution sequences"):
  with members:
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "attention is all you need"
    - "deepsign deep learning for automatic malware signature generation and classification"
    - "deep neural network based malware detection using two dimensional binary program features"
    - "adam a method for stochastic optimization"
    - "on the properties of neural machine translation encoder decoder approaches"

Connected through hyperedge HE-3 (induced by source paper "early stage malware prediction using recurrent neural networks"):
  with members:
    - "lstm a search space odyssey"
    - "generic black box end to end attack against state of the art api call based malware classifiers"
    - "adversarial perturbations against deep neural networks for malware classification"
    - "a critical review of recurrent neural networks for sequence learning"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "deep neural network based malware detection using two dimensional binary program features"
    - "adam a method for stochastic optimization"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"

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)