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: "automatic detection of malware generated domains with recurrent neural models"
  Abstract: Modern malware families often rely on domain-generation algorithms (DGAs) to determine rendezvous points to their command-and-control server. Traditional defence strategies (such as blacklisting domains or IP addresses) are inadequate against such techniques due to the large and continuously changing list of domains produced by these algorithms. This paper demonstrates that a machine learning approach based on recurrent neural networks is able to detect domain names generated by DGAs with high precision. The neural models are estimated on a large training set of domains generated by various malwares. Experimental results show that this data-driven approach can detect malware-generated domain names with a F_1 score of 0.971. To put it differently, the model can automatically detect 93 % of malware-generated domain names for a false positive rate of 1:100.

Connected through hyperedge HE-1 (induced by source paper "charbot a simple and effective method for evading dga classifiers"):
  with members:
    - "maskdga a black box evasion technique against dga classifiers and adversarial defenses"
    - "inline detection of domain generation algorithms with context sensitive word embeddings"
    - "semidefinite relaxations for certifying robustness to adversarial examples"
    - "adversarial texts with gradient methods"
    - "passgan a deep learning approach for password guessing"
    - "expose a character level convolutional neural network with embeddings for detecting malicious urls file paths and registry keys"
    - "generating adversarial malware examples for black box attacks based on gan"
    - "predicting domain generation algorithms with long short term memory networks"

Connected through hyperedge HE-2 (induced by source paper "exploiting statistical and structural features for the detection of domain generation algorithms"):
  with members:
    - "encrypted and covert dns queries for botnets challenges and countermeasures"
    - "hydras and ipfs a decentralised playground for malware"
    - "detecting dga domains with recurrent neural networks and side information"
    - "inline detection of domain generation algorithms with context sensitive word embeddings"
    - "deep contextualized word representations"
    - "a survey on malicious domains detection through dns data analysis"
    - "don t skype type acoustic eavesdropping in voice over ip"
    - "deepdga adversarially tuned domain generation and detection"

Connected through hyperedge HE-3 (induced by source paper "a compression based framework for the detection of anomalies in heterogeneous data sources"):
  with members:
    - "an adaptive system for detecting malicious queries in web attacks"
    - "predicting cyber events by leveraging hacker sentiment"
    - "shallow and deep networks intrusion detection system a taxonomy and survey"
    - "current challenges and future research areas for digital forensic investigation"
    - "predicting domain generation algorithms with long short term memory networks"

Connected through hyperedge HE-4 (induced by source paper "hindom a robust malicious domain detection system based on heterogeneous information network with transductive classification"):
  with members:
    - "meta path guided embedding for similarity search in large scale heterogeneous information networks"
    - "predicting domain generation algorithms with long short term memory networks"
    - "deepdga adversarially tuned domain generation and detection"
    - "a survey of heterogeneous information network analysis"

Connected through hyperedge HE-5 (induced by source paper "image based feature representation for insider threat classification"):
  with members:
    - "transfer learning for image based malware classification"
    - "classifier suites for insider threat detection"
    - "insight into insiders and it a survey of insider threat taxonomies analysis modeling and countermeasures"

Connected through hyperedge HE-6 (induced by source paper "inline detection of domain generation algorithms with context sensitive word embeddings"):
  with members:
    - "detecting dga domains with recurrent neural networks and side information"
    - "deep contextualized word representations"
    - "a survey on malicious domains detection through dns data analysis"
    - "universal language model fine tuning for text classification"
    - "learned in translation contextualized word vectors"
    - "predicting domain generation algorithms with long short term memory networks"
    - "one billion word benchmark for measuring progress in statistical language modeling"

Connected through hyperedge HE-7 (induced by source paper "a short review on applications of deep learning for cyber security"):
  with members:
    - "a brief survey on autonomous vehicle possible attacks exploits and vulnerabilities"
    - "deepimagespam deep learning based image spam detection"
    - "lstm based system call language modeling and robust ensemble method for designing host based intrusion detection systems"
    - "predicting domain generation algorithms with long short term memory networks"
    - "tensorflow a system for large scale machine learning"
    - "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.CR(Cryptography and Security)