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: "a multi view context aware approach to android malware detection and malicious code localization"
  Abstract: Existing Android malware detection approaches use a variety of features such as security sensitive APIs, system calls, control-flow structures and information flows in conjunction with Machine Learning classifiers to achieve accurate detection. Each of these feature sets provides a unique semantic perspective (or view) of apps' behaviours with inherent strengths and limitations. Meaning, some views are more amenable to detect certain attacks but may not be suitable to characterise several other attacks. Most of the existing malware detection approaches use only one (or a selected few) of the aforementioned feature sets which prevent them from detecting a vast majority of attacks. Addressing this limitation, we propose MKLDroid, a unified framework that systematically integrates multiple views of apps for performing comprehensive malware detection and malicious code localisation. The rationale is that, while a malware app can disguise itself in some views, disguising in every view while maintaining malicious intent will be much harder. #R##N#MKLDroid uses a graph kernel to capture structural and contextual information from apps' dependency graphs and identify malice code patterns in each view. Subsequently, it employs Multiple Kernel Learning (MKL) to find a weighted combination of the views which yields the best detection accuracy. Besides multi-view learning, MKLDroid's unique and salient trait is its ability to locate fine-grained malice code portions in dependency graphs (e.g., methods/classes). Through our large-scale experiments on several datasets (incl. wild apps), we demonstrate that MKLDroid outperforms three state-of-the-art techniques consistently, in terms of accuracy while maintaining comparable efficiency. In our malicious code localisation experiments on a dataset of repackaged malware, MKLDroid was able to identify all the malice classes with 94% average recall.

Connected through hyperedge HE-1 (induced by source paper "droidetec android malware detection and malicious code localization through deep learning"):
  with members:
    - "android hiv a study of repackaging malware for evading machine learning detection"
    - "attention is all you need"
    - "android malware clustering through malicious payload mining"
    - "combining language and vision with a multimodal skip gram model"
    - "recurrent models of visual attention"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-2 (induced by source paper "learning from context exploiting and interpreting file path information for better malware detection"):
  with members:
    - "aloha auxiliary loss optimization for hypothesis augmentation"
    - "ember an open dataset for training static pe malware machine learning models"
    - "meade towards a malicious email attachment detection engine"
    - "a deep learning approach to fast format agnostic detection of malicious web content"
    - "microsoft malware classification challenge"
    - "expose a character level convolutional neural network with embeddings for detecting malicious urls file paths and registry keys"
    - "in2i unsupervised multi image to image translation using generative adversarial networks"
    - "a survey of stealth malware attacks mitigation measures and steps toward autonomous open world solutions"

Connected through hyperedge HE-3 (induced by source paper "mobile app privacy in software engineering research a systematic mapping study"):
  with members:
    - "static analysis for extracting permission checks of a large scale framework the challenges and solutions for analyzing android"

Connected through hyperedge HE-4 (induced by source paper "apk2vec semi supervised multi view representation learning for profiling android applications"):
  with members:
    - "graph embedding techniques applications and performance a survey"
    - "anonymous walk embeddings"
    - "struc2vec learning node representations from structural identity"
    - "hash embeddings for efficient word representations"
    - "deep graph attention model"
    - "graph2vec learning distributed representations of graphs"
    - "contextual weisfeiler lehman graph kernel for malware detection"
    - "node2vec scalable feature learning for networks"

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)