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: "an evasion and counter evasion study in malicious websites detection"
  Abstract: Malicious websites are a major cyber attack vector, and effective detection of them is an important cyber defense task. The main defense paradigm in this regard is that the defender uses some kind of machine learning algorithms to train a detection model, which is then used to classify websites in question. Unlike other settings, the following issue is inherent to the problem of malicious websites detection: the attacker essentially has access to the same data that the defender uses to train its detection models. This 'symmetry' can be exploited by the attacker, at least in principle, to evade the defender's detection models. In this paper, we present a framework for characterizing the evasion and counter-evasion interactions between the attacker and the defender, where the attacker attempts to evade the defender's detection models by taking advantage of this symmetry. Within this framework, we show that an adaptive attacker can make malicious websites evade powerful detection models, but proactive training can be an effective counter-evasion defense mechanism. The framework is geared toward the popular detection model of decision tree, but can be adapted to accommodate other classifiers.

Connected through hyperedge HE-1 (induced by source paper "taking care of the discretization problem a black box adversarial image attack in discrete integer domain"):
  with members:
    - "towards improved testing for deep learning"
    - "one pixel attack for fooling deep neural networks"
    - "procedural noise adversarial examples for black box attacks on deep convolutional networks"
    - "on the design of black box adversarial examples by leveraging gradient free optimization and operator splitting method"
    - "autozoom autoencoder based zeroth order optimization method for attacking black box neural networks"
    - "spatially transformed adversarial examples"
    - "formal security analysis of neural networks using symbolic intervals"
    - "commandersong a systematic approach for practical adversarial voice recognition"

Connected through hyperedge HE-2 (induced by source paper "a case study on using deep learning for network intrusion detection"):
  with members:
    - "metrics towards measuring cyber agility"
    - "vuldeepecker a deep learning based system for vulnerability detection"
    - "a novel hierarchical intrusion detection system based on decision tree and rules based models"
    - "characterizing honeypot captured cyber attacks statistical framework and case study"
    - "entity embeddings of categorical variables"
    - "predicting cyber attack rates with extreme values"
    - "preventive and reactive cyber defense dynamics is globally stable"
    - "spatiotemporal patterns and predictability of cyberattacks"

Connected through hyperedge HE-3 (induced by source paper "sok a comprehensive reexamination of phishing research from the security perspective"):
  with members:
    - "deltaphish detecting phishing webpages in compromised websites"
    - "defending against phishing attacks taxonomy of methods current issues and future directions"
    - "malicious url detection using machine learning a survey"
    - "an intelligent classification model for phishing email detection"
    - "do users focus on the correct cues to differentiate between phishing and genuine emails"
    - "know your phish novel techniques for detecting phishing sites and their targets"
    - "bit ly malicious deep dive into short url based e crime detection"
    - "phishari automatic realtime phishing detection on twitter"

Connected through hyperedge HE-4 (induced by source paper "analyzing root causes of intrusion detection false negatives methodology and case study"):
  with members:
    - "metrics towards measuring cyber agility"
    - "benchmarking datasets for anomaly based network intrusion detection kdd cup 99 alternatives"
    - "a taxonomy and survey of intrusion detection system design techniques network threats and datasets"
    - "characterizing honeypot captured cyber attacks statistical framework and case study"
    - "predicting cyber attack rates with extreme values"

Connected through hyperedge HE-5 (induced by source paper "hashtran dnn a framework for enhancing robustness of deep neural networks against adversarial malware samples"):
  with members:
    - "dense associative memory is robust to adversarial inputs"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "magnet a two pronged defense against adversarial examples"
    - "adversarial perturbations against deep neural networks for malware classification"
    - "transferability in machine learning from phenomena to black box attacks using adversarial samples"
    - "delving into transferable adversarial examples and black box attacks"
    - "towards evaluating the robustness of neural networks"
    - "adversarial machine learning at scale"

Connected through hyperedge HE-6 (induced by source paper "enhancing robustness of deep neural networks against adversarial malware samples principles framework and aics 2019 challenge"):
  with members:
    - "adversarial deep learning for robust detection of binary encoded malware"
    - "hashtran dnn a framework for enhancing robustness of deep neural networks against adversarial malware samples"
    - "why do adversarial attacks transfer explaining transferability of evasion and poisoning attacks"
    - "a systematic study of the class imbalance problem in convolutional neural networks"
    - "smart augmentation learning an optimal data augmentation strategy"
    - "generating adversarial malware examples for black box attacks based on gan"
    - "towards evaluating the robustness of neural networks"
    - "adversarial examples in the physical world"

Connected through hyperedge HE-7 (induced by source paper "security theater on the vulnerability of classifiers to exploratory attacks"):
  with members:
    - "security evaluation of pattern classifiers under attack"
    - "transferability in machine learning from phenomena to black box attacks using adversarial samples"
    - "stealing machine learning models via prediction apis"
    - "membership inference attacks against machine learning models"
    - "the limitations of deep learning in adversarial settings"
    - "evasion attacks against machine learning at test time"
    - "scikit learn machine learning in python"
    - "near optimal evasion of convex inducing classifiers"

Connected through hyperedge HE-8 (induced by source paper "optimizing top precision performance measure of content based image retrieval by learning similarity function"):
  with members:
    - "supervised learning of sparse context reconstruction coefficients for data representation and classification"
    - "push and pull based epidemic spreading in networks thresholds and deeper insights"
    - "representing data by sparse combination of contextual data points for classification"
    - "remote health coaching system and human motion data analysis for physical therapy with microsoft kinect"
    - "evaluation of pose tracking accuracy in the first and second generations of microsoft kinect"
    - "image tag completion by local learning"
    - "supervised cross modal factor analysis for multiple modal data classification"
    - "unsupervised temporal segmentation of repetitive human actions based on kinematic modeling and frequency analysis"

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)