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: "deepdga adversarially tuned domain generation and detection"
  Abstract: Many malware families utilize domain generation algorithms (DGAs) to establish command and control (C&C) connections. While there are many methods to pseudorandomly generate domains, we focus in this paper on detecting (and generating) domains on a per-domain basis which provides a simple and flexible means to detect known DGA families. Recent machine learning approaches to DGA detection have been successful on fairly simplistic DGAs, many of which produce names of fixed length. However, models trained on limited datasets are somewhat blind to new DGA variants. #R##N#In this paper, we leverage the concept of generative adversarial networks to construct a deep learning based DGA that is designed to intentionally bypass a deep learning based detector. In a series of adversarial rounds, the generator learns to generate domain names that are increasingly more difficult to detect. In turn, a detector model updates its parameters to compensate for the adversarially generated domains. We test the hypothesis of whether adversarially generated domains may be used to augment training sets in order to harden other machine learning models against yet-to-be-observed DGAs. We detail solutions to several challenges in training this character-based generative adversarial network (GAN). In particular, our deep learning architecture begins as a domain name auto-encoder (encoder + decoder) trained on domains in the Alexa one million. Then the encoder and decoder are reassembled competitively in a generative adversarial network (detector + generator), with novel neural architectures and training strategies to improve convergence.

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"
    - "inline detection of domain generation algorithms with context sensitive word embeddings"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "encrypted and covert dns queries for botnets challenges and countermeasures"):
  with members:
    - "hydras and ipfs a decentralised playground for malware"
    - "hedge efficient traffic classification of encrypted and compressed packets"
    - "detecting dga domains with recurrent neural networks and side information"
    - "a survey on malicious domains detection through dns data analysis"

Connected through hyperedge HE-3 (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"
    - "inline detection of domain generation algorithms with context sensitive word embeddings"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "ten ai stepping stones for cybersecurity"):
  with members:
    - "machine learning ddos detection for consumer internet of things devices"
    - "adversarial deep learning for robust detection of binary encoded malware"
    - "cache telepathy leveraging shared resource attacks to learn dnn architectures"
    - "provable limitations of deep learning"
    - "adversarial malware binaries evading deep learning for malware detection in executables"
    - "a first look at deep learning apps on smartphones"
    - "prada protecting against dnn model stealing attacks"
    - "logan membership inference attacks against generative models"

Connected through hyperedge HE-5 (induced by source paper "flow based network traffic generation using generative adversarial networks"):
  with members:
    - "pros and cons of gan evaluation measures"
    - "frechet chemblnet distance a metric for generative models for molecules"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "zipnet gan inferring fine grained mobile traffic patterns via a generative adversarial neural network"
    - "improved training of wasserstein gans"
    - "generating adversarial malware examples for black box attacks based on gan"
    - "seqgan sequence generative adversarial nets with policy gradient"
    - "nips 2016 tutorial generative adversarial networks"

Connected through hyperedge HE-6 (induced by source paper "hardening random forest cyber detectors against adversarial attacks"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "sok security and privacy in machine learning"
    - "yes machine learning can be more secure a case study on android malware detection"
    - "improving the adversarial robustness and interpretability of deep neural networks by regularizing their input gradients"
    - "on security and sparsity of linear classifiers for adversarial settings"
    - "generating adversarial malware examples for black box attacks based on gan"
    - "adversarial perturbations against deep neural networks for malware classification"
    - "towards evaluating the robustness of neural networks"

Connected through hyperedge HE-7 (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"
    - "automatic detection of malware generated domains with recurrent neural models"

Connected through hyperedge HE-8 (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"
    - "automatic detection of malware generated domains with recurrent neural models"
    - "don t skype type acoustic eavesdropping in voice over ip"

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)