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: "convolutional neural networks over control flow graphs for software defect prediction"
  Abstract: Existing defects in software components is unavoidable and leads to not only a waste of time and money but also many serious consequences. To build predictive models, previous studies focus on manually extracting features or using tree representations of programs, and exploiting different machine learning algorithms. However, the performance of the models is not high since the existing features and tree structures often fail to capture the semantics of programs. To explore deeply programs' semantics, this paper proposes to leverage precise graphs representing program execution flows, and deep neural networks for automatically learning defect features. Firstly, control flow graphs are constructed from the assembly instructions obtained by compiling source code; we thereafter apply multi-view multi-layer directed graph-based convolutional neural networks (DGCNNs) to learn semantic features. The experiments on four real-world datasets show that our method significantly outperforms the baselines including several other deep learning approaches.

Connected through hyperedge HE-1 (induced by source paper "detecting code clones with graph neural networkand flow augmented abstract syntax tree"):
  with members:
    - "fast graph representation learning with pytorch geometric"
    - "graph matching networks for learning the similarity of graph structured objects"
    - "graph neural networks a review of methods and applications"
    - "oreo detection of clones in the twilight zone"
    - "generative code modeling with graphs"
    - "neural message passing for quantum chemistry"
    - "learning to represent programs with graphs"
    - "gated graph sequence neural networks"

Connected through hyperedge HE-2 (induced by source paper "using deep learning to solve computer security challenges a survey"):
  with members:
    - "deepcheck a non intrusive control flow integrity checking based on deep learning"
    - "deep reinforcement fuzzing"
    - "recurrent neural network attention mechanisms for interpretable system log anomaly detection"
    - "neuzz efficient fuzzing with neural program smoothing"
    - "ropnn detection of rop payloads using deep neural networks"
    - "neural network based graph embedding for cross platform binary code similarity detection"
    - "not all bytes are equal neural byte sieve for fuzzing"
    - "generic black box end to end attack against state of the art api call based malware classifiers"

Connected through hyperedge HE-3 (induced by source paper "applications of graph integration to function comparison and malware classification"):
  with members:
    - "explaining vulnerabilities of deep learning to adversarial malware binaries"
    - "exploring adversarial examples in malware detection"
    - "graph embedding techniques applications and performance a survey"
    - "android malware detection based on factorization machine"
    - "neural network based graph embedding for cross platform binary code similarity detection"
    - "adversarial perturbations against deep neural networks for malware 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.SE(Software Engineering)