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: "reviewer integration and performance measurement for malware detection"
  Abstract: We present and evaluate a large-scale malware detection system integrating machine learning with expert reviewers, treating reviewers as a limited labeling resource. We demonstrate that even in small numbers, reviewers can vastly improve the system's ability to keep pace with evolving threats. We conduct our evaluation on a sample of VirusTotal submissions spanning 2.5 years and containing 1.1 million binaries with 778GB of raw feature data. Without reviewer assistance, we achieve 72% detection at a 0.5% false positive rate, performing comparable to the best vendors on VirusTotal. Given a budget of 80 accurate reviews daily, we improve detection to 89% and are able to detect 42% of malicious binaries undetected upon initial submission to VirusTotal. Additionally, we identify a previously unnoticed temporal inconsistency in the labeling of training datasets. We compare the impact of training labels obtained at the same time training data is first seen with training labels obtained months later. We find that using training labels obtained well after samples appear, and thus unavailable in practice for current training data, inflates measured detection by almost 20 percentage points. We release our cluster-based implementation, as well as a list of all hashes in our evaluation and 3% of our entire dataset.

Connected through hyperedge HE-1 (induced by source paper "intriguing properties of adversarial ml attacks in the problem space"):
  with members:
    - "misleading authorship attribution of source code using adversarial learning"
    - "on evaluating adversarial robustness"
    - "tesseract eliminating experimental bias in malware classification across space and time"
    - "evading classifiers in discrete domains with provable optimality guarantees"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "textbugger generating adversarial text against real world applications"
    - "generating natural language adversarial examples"
    - "adversarial malware binaries evading deep learning for malware detection in executables"

Connected through hyperedge HE-2 (induced by source paper "detecting and characterizing lateral phishing at scale"):
  with members:
    - "tesseract eliminating experimental bias in malware classification across space and time"

Connected through hyperedge HE-3 (induced by source paper "static malware detection subterfuge quantifying the robustness of machine learning and current anti virus"):
  with members:
    - "adversarial malware binaries evading deep learning for malware detection in executables"
    - "adversarial perturbations against deep neural networks for malware classification"
    - "from malware signatures to anti virus assisted attacks"
    - "transferability in machine learning from phenomena to black box attacks using adversarial samples"
    - "deep neural network based malware detection using two dimensional binary program features"
    - "evasion attacks against machine learning at test time"
    - "visualizing and understanding convolutional networks"
    - "intriguing properties of neural networks"

Connected through hyperedge HE-4 (induced by source paper "tesseract eliminating experimental bias in malware classification across space and time"):
  with members:
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "explaining black box android malware detection"
    - "benchmarking crimes an emerging threat in systems security"
    - "mamadroid detecting android malware by building markov chains of behavioral models"
    - "why should i trust you explaining the predictions of any classifier"
    - "learning when training data are costly the effect of class distribution on tree induction"

Connected through hyperedge HE-5 (induced by source paper "eight years of rider measurement in the android malware ecosystem evolution and lessons learned"):
  with members:
    - "tesseract eliminating experimental bias in malware classification across space and time"
    - "beyond google play a large scale comparative study of chinese android app markets"
    - "a large scale investigation of obfuscation use in google play"
    - "mamadroid detecting android malware by building markov chains of behavioral models"
    - "an investigation into the use of common libraries in android apps"

Connected through hyperedge HE-6 (induced by source paper "the shape of alerts detecting malware using distributed detectors by robustly amplifying transient correlations"):
  with members:
    - "catching worms trojan horses and pups unsupervised detection of silent delivery campaigns"
    - "practical black box attacks against machine learning"
    - "xgboost a scalable tree boosting system"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-7 (induced by source paper "on the statistical detection of adversarial examples"):
  with members:
    - "sok security and privacy in machine learning"
    - "adversarial perturbations against deep neural networks for malware classification"
    - "practical black box attacks against machine learning"
    - "transferability in machine learning from phenomena to black box attacks using adversarial samples"
    - "adversarial examples detection in deep networks with convolutional filter statistics"
    - "towards evaluating the robustness of neural networks"
    - "towards open set deep networks"
    - "distillation as a defense to adversarial perturbations against deep neural networks"

Connected through hyperedge HE-8 (induced by source paper "exploiting latent attack semantics for intelligent malware detection"):
  with members:
    - "practical black box attacks against machine learning"
    - "evolution of conversations in the age of email overload"
    - "detection of early stage enterprise infection by mining large scale log data"
    - "a tutorial on spectral clustering"

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)