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: "passgan a deep learning approach for password guessing"
  Abstract: State-of-the-art password guessing tools, such as HashCat and John the Ripper, enable users to check billions of passwords per second against password hashes. In addition to performing straightforward dictionary attacks, these tools can expand password dictionaries using password generation rules, such as concatenation of words (e.g., "password123456") and leet speak (e.g., "password" becomes "p4s5w0rd"). Although these rules work well in practice, expanding them to model further passwords is a laborious task that requires specialized expertise. To address this issue, in this paper we introduce PassGAN, a novel approach that replaces human-generated password rules with theory-grounded machine learning algorithms. Instead of relying on manual password analysis, PassGAN uses a Generative Adversarial Network (GAN) to autonomously learn the distribution of real passwords from actual password leaks, and to generate high-quality password guesses. Our experiments show that this approach is very promising. When we evaluated PassGAN on two large password datasets, we were able to surpass rule-based and state-of-the-art machine learning password guessing tools. However, in contrast with the other tools, PassGAN achieved this result without any a-priori knowledge on passwords or common password structures. Additionally, when we combined the output of PassGAN with the output of HashCat, we were able to match 51%-73% more passwords than with HashCat alone. This is remarkable, because it shows that PassGAN can autonomously extract a considerable number of password properties that current state-of-the art rules do not encode.

Connected through hyperedge HE-1 (induced by source paper "improving password guessing via representation learning"):
  with members:
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "graph embedding techniques applications and performance a survey"
    - "large scale gan training for high fidelity natural image synthesis"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "learning inverse mapping by autoencoder based generative adversarial nets"
    - "sampling generative networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-2 (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"
    - "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-3 (induced by source paper "a deep learning based attack for the chaos based image encryption"):
  with members:
    - "decoupled weight decay regularization"
    - "deep predictive coding networks for video prediction and unsupervised learning"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "end to end training of deep visuomotor policies"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "pilot password and pin information leakage from obfuscated typing videos"):
  with members:
    - "don t skype type acoustic eavesdropping in voice over ip"

Connected through hyperedge HE-5 (induced by source paper "the reincarnation of grille cipher"):
  with members:
    - "an introduction to image synthesis with generative adversarial nets"
    - "learning to protect communications with adversarial neural cryptography"
    - "ssgan secure steganography based on generative adversarial networks"
    - "steganographic generative adversarial networks"
    - "generative steganography with kerckhoffs principle based on generative adversarial networks"
    - "semantic image inpainting with deep generative models"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"

Connected through hyperedge HE-6 (induced by source paper "exploring the impact of password dataset distribution on guessing"):
  with members:
    - "evaluating password advice"
    - "investigating the distribution of password choices"

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)