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: "deepsign deep learning for automatic malware signature generation and classification"
  Abstract: This paper presents a novel deep learning based method for automatic malware signature generation and classification. The method uses a deep belief network (DBN), implemented with a deep stack of denoising autoencoders, generating an invariant compact representation of the malware behavior. While conventional signature and token based methods for malware detection do not detect a majority of new variants for existing malware, the results presented in this paper show that signatures generated by the DBN allow for an accurate classification of new malware variants. Using a dataset containing hundreds of variants for several major malware families, our method achieves 98.6% classification accuracy using the signatures generated by the DBN. The presented method is completely agnostic to the type of malware behavior that is logged (e.g., API calls and their parameters, registry entries, websites and ports accessed, etc.), and can use any raw input from a sandbox to successfully train the deep neural network which is used to generate malware signatures.

Connected through hyperedge HE-1 (induced by source paper "a roadmap toward the resilient internet of things for cyber physical systems"):
  with members:
    - "model based verification and validation of an autonomous vehicle system"
    - "deepmarks a digital fingerprinting framework for deep neural networks"
    - "mlcapsule guarded offline deployment of machine learning as a service"
    - "reachability analysis and safety verification for neural network control systems"
    - "introduction of a hybrid monitor to cyber physical systems"
    - "verification for machine learning autonomy and neural networks survey"
    - "re purposing compact neuronal circuit policies to govern reinforcement learning tasks"
    - "efficient deep learning on multi source private data"

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"
    - "convolutional neural networks over control flow graphs for software defect prediction"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "explaining vulnerabilities of deep learning to adversarial malware binaries"):
  with members:
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "ember an open dataset for training static pe malware machine learning models"
    - "adversarial malware binaries evading deep learning for malware detection in executables"
    - "axiomatic attribution for deep networks"
    - "adversarial perturbations against deep neural networks for malware classification"
    - "why should i trust you explaining the predictions of any classifier"
    - "towards evaluating the robustness of neural networks"
    - "deep neural network based malware detection using two dimensional binary program features"

Connected through hyperedge HE-4 (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"
    - "virtual machine introspection based malware behavior profiling and family grouping"
    - "attention is all you need"
    - "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-5 (induced by source paper "dynamic malware analysis with feature engineering and feature learning"):
  with members:
    - "anmm ranking short answer texts with attention based neural matching model"
    - "session aware information embedding for e commerce product recommendation"
    - "language modeling with gated convolutional networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "malicious behavior detection using windows audit logs"
    - "feature hashing for large scale multitask learning"

Connected through hyperedge HE-6 (induced by source paper "malware classification using deep learning based feature extraction and wrapper based feature selection technique"):
  with members:
    - "detecting and classifying android malware using static analysis along with creator information"
    - "a survey of the recent architectures of deep convolutional neural networks"
    - "adversarial deep learning for robust detection of binary encoded malware"
    - "novel feature extraction selection and fusion for effective malware family classification"
    - "efficient estimation of word representations in vector space"
    - "malware classification based on call graph clustering"

Connected through hyperedge HE-7 (induced by source paper "the curious case of machine learning in malware detection"):
  with members:
    - "adversarial malware binaries evading deep learning for malware detection in executables"
    - "developing a k ary malware using blockchain"
    - "adaptive and scalable android malware detection through online learning"

Connected through hyperedge HE-8 (induced by source paper "deep learning at the shallow end malware classification for non domain experts"):
  with members:
    - "microsoft malware classification challenge"
    - "network intell enabling the non expert analysis of large volumes of intercepted network traffic"
    - "recent trends in deep learning based natural language processing"
    - "increasing digital investigator availability through efficient workflow management and automation"
    - "integration of ether unpacker into ragpicker for plugin based malware analysis and identification"
    - "battling the digital forensic backlog through data deduplication"
    - "tiered forensic methodology model for digital field triage by non digital evidence specialists"
    - "deep learning in neural 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)