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: "contextual weisfeiler lehman graph kernel for malware detection"
  Abstract: In this paper, we propose a novel graph kernel specifically to address a challenging problem in the field of cyber-security, namely, malware detection. Previous research has revealed the following: (1) Graph representations of programs are ideally suited for malware detection as they are robust against several attacks, (2) Besides capturing topological neighbourhoods (i.e., structural information) from these graphs it is important to capture the context under which the neighbourhoods are reachable to accurately detect malicious neighbourhoods. #R##N#We observe that state-of-the-art graph kernels, such as Weisfeiler-Lehman kernel (WLK) capture the structural information well but fail to capture contextual information. To address this, we develop the Contextual Weisfeiler-Lehman kernel (CWLK) which is capable of capturing both these types of information. We show that for the malware detection problem, CWLK is more expressive and hence more accurate than WLK while maintaining comparable efficiency. Through our large-scale experiments with more than 50,000 real-world Android apps, we demonstrate that CWLK outperforms two state-of-the-art graph kernels (including WLK) and three malware detection techniques by more than 5.27% and 4.87% F-measure, respectively, while maintaining high efficiency. This high accuracy and efficiency make CWLK suitable for large-scale real-world malware detection.

Connected through hyperedge HE-1 (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"
    - "a multi view context aware approach to android malware detection and malicious code localization"
    - "deep graph attention model"
    - "graph2vec learning distributed representations of graphs"
    - "node2vec scalable feature learning for networks"

Connected through hyperedge HE-2 (induced by source paper "a multi view context aware approach to android malware detection and malicious code localization"):
  with members:
    - "poisoning behavioral malware clustering"
    - "a unifying view of explicit and implicit feature maps for structured data systematic studies of graph kernels"
    - "mamadroid detecting android malware by building markov chains of behavioral models"
    - "adaptive and scalable android malware detection through online learning"
    - "why should i trust you explaining the predictions of any classifier"
    - "subgraph2vec learning distributed representations of rooted sub graphs from large graphs"

Connected through hyperedge HE-3 (induced by source paper "androvault constructing knowledge graph from millions of android apps for automated analysis"):
  with members:
    - "androzoo collecting millions of android apps and their metadata for the research community"
    - "mamadroid detecting android malware by building markov chains of behavioral models"

Connected through hyperedge HE-4 (induced by source paper "subgraph2vec learning distributed representations of rooted sub graphs from large graphs"):
  with members:
    - "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)