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: "predicting domain generation algorithms with long short term memory networks"
  Abstract: Various families of malware use domain generation algorithms (DGAs) to generate a large number of pseudo-random domain names to connect to a command and control (C&C) server. In order to block DGA C&C traffic, security organizations must first discover the algorithm by reverse engineering malware samples, then generating a list of domains for a given seed. The domains are then either preregistered or published in a DNS blacklist. This process is not only tedious, but can be readily circumvented by malware authors using a large number of seeds in algorithms with multivariate recurrence properties (e.g., banjori) or by using a dynamic list of seeds (e.g., bedep). Another technique to stop malware from using DGAs is to intercept DNS queries on a network and predict whether domains are DGA generated. Such a technique will alert network administrators to the presence of malware on their networks. In addition, if the predictor can also accurately predict the family of DGAs, then network administrators can also be alerted to the type of malware that is on their networks. This paper presents a DGA classifier that leverages long short-term memory (LSTM) networks to predict DGAs and their respective families without the need for a priori feature extraction. Results are significantly better than state-of-the-art techniques, providing 0.9993 area under the receiver operating characteristic curve for binary classification and a micro-averaged F1 score of 0.9906. In other terms, the LSTM technique can provide a 90% detection rate with a 1:10000 false positive (FP) rate---a twenty times FP improvement over comparable methods. Experiments in this paper are run on open datasets and code snippets are provided to reproduce the results.

Connected through hyperedge HE-1 (induced by source paper "nested multiple instance learning in modelling of http network traffic"):
  with members:
    - "approximation capability of neural networks on spaces of probability measures and tree structured domains"
    - "discriminative models for multi instance problems with tree structure"
    - "expose a character level convolutional neural network with embeddings for detecting malicious urls file paths and registry keys"
    - "using neural network formalism to solve multiple instance problems"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "julia a fresh approach to numerical computing"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-2 (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"
    - "deepdga adversarially tuned domain generation and detection"

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 "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-5 (induced by source paper "domaingan generating adversarial examples to attack domain generation algorithm classifiers"):
  with members:
    - "maskdga a black box evasion technique against dga classifiers and adversarial defenses"
    - "charbot a simple and effective method for evading dga classifiers"
    - "which training methods for gans do actually converge"
    - "least squares generative adversarial networks"
    - "deepdga adversarially tuned domain generation and detection"
    - "convolutional neural networks for sentence classification"

Connected through hyperedge HE-6 (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"
    - "automatic detection of malware generated domains with recurrent neural models"
    - "current challenges and future research areas for digital forensic investigation"

Connected through hyperedge HE-7 (induced by source paper "hindom a robust malicious domain detection system based on heterogeneous information network with transductive classification"):
  with members:
    - "automatic detection of malware generated domains with recurrent neural models"
    - "meta path guided embedding for similarity search in large scale heterogeneous information networks"
    - "deepdga adversarially tuned domain generation and detection"
    - "a survey of heterogeneous information network analysis"

Connected through hyperedge HE-8 (induced by source paper "an ai based multi stage detection system of banking botnets"):
  with members:
    - "a cyber kill chain based taxonomy of banking trojans for evolutionary computational intelligence"

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)