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: "inline detection of domain generation algorithms with context sensitive word embeddings"
  Abstract: Domain generation algorithms (DGAs) are frequently employed by malware to generate domains used for connecting to command-and-control (C2) servers. Recent work in DGA detection leveraged deep learning architectures like convolutional neural networks (CNNs) and character-level long short-term memory networks (LSTMs) to classify domains. However, these classifiers perform poorly with wordlist-based DGA families, which generate domains by pseudorandomly concatenating dictionary words. We propose a novel approach that combines context-sensitive word embeddings with a simple fully-connected classifier to perform classification of domains based on word-level information. The word embeddings were pre-trained on a large unrelated corpus and left frozen during the training on domain data. The resulting small number of trainable parameters enabled extremely short training durations, while the transfer of language knowledge stored in the representations allowed for high-performing models with small training datasets. We show that this architecture reliably outperformed existing techniques on wordlist-based DGA families with just 30 DGA training examples and achieved state-of-the-art performance with around 100 DGA training examples, all while requiring an order of magnitude less time to train compared to current techniques. Of special note is the technique’s performance on the matsnu DGA: the classifier attained a 89.5% detection rate with a 1:1,000 false positive rate (FPR) after training on only 30 examples of the DGA domains, and a 91.2% detection rate with a 1:10,000 FPR after 90 examples. Considering that some of these DGAs have wordlists of several hundred words, our results demonstrate that this technique does not rely on the classifier learning the DGA wordlists. Instead, the classifier is able to learn the semantic signatures of the wordlist-based DGA families.

Connected through hyperedge HE-1 (induced by source paper "inline detection of dga domains using side information"):
  with members:
    - "maskdga a black box evasion technique against dga classifiers and adversarial defenses"
    - "using lexical features for malicious url detection a machine learning approach"
    - "charbot a simple and effective method for evading dga classifiers"
    - "detecting dga domains with recurrent neural networks and side information"
    - "expose a character level convolutional neural network with embeddings for detecting malicious urls file paths and registry keys"
    - "predicting domain generation algorithms with long short term memory networks"
    - "deepdga adversarially tuned domain generation and detection"

Connected through hyperedge HE-2 (induced by source paper "maskdga a black box evasion technique against dga classifiers and adversarial defenses"):
  with members:
    - "detecting dga domains with recurrent neural networks and side information"
    - "black box generation of adversarial text sequences to evade deep learning classifiers"
    - "exploring adversarial examples in malware detection"
    - "detection of malicious and low throughput data exfiltration over the dns protocol"
    - "expose a character level convolutional neural network with embeddings for detecting malicious urls file paths and registry keys"
    - "hotflip white box adversarial examples for text classification"
    - "generating adversarial malware examples for black box attacks based on gan"
    - "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 "charbot a simple and effective method for evading dga classifiers"):
  with members:
    - "maskdga a black box evasion technique against dga classifiers and adversarial defenses"
    - "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"
    - "automatic detection of malware generated domains with recurrent neural models"
    - "predicting domain generation algorithms with long short term memory networks"

Connected through hyperedge HE-4 (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"
    - "deep contextualized word representations"
    - "a survey on malicious domains detection through dns data analysis"
    - "automatic detection of malware generated domains with recurrent neural models"
    - "don t skype type acoustic eavesdropping in voice over ip"
    - "deepdga adversarially tuned domain generation and detection"

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)