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: "tesseract eliminating experimental bias in malware classification across space and time"
  Abstract: Is Android malware classification a solved problem? Published F1 scores of up to 0.99 appear to leave very little room for improvement. In this paper, we argue that results are commonly inflated due to two pervasive sources of experimental bias: "spatial bias" caused by distributions of training and testing data that are not representative of a real-world deployment; and "temporal bias" caused by incorrect time splits of training and testing sets, leading to impossible configurations. We propose a set of space and time constraints for experiment design that eliminates both sources of bias. We introduce a new metric that summarizes the expected robustness of a classifier in a real-world setting, and we present an algorithm to tune its performance. Finally, we demonstrate how this allows us to evaluate mitigation strategies for time decay such as active learning. We have implemented our solutions in TESSERACT, an open source evaluation framework for comparing malware classifiers in a realistic setting. We used TESSERACT to evaluate three Android malware classifiers from the literature on a dataset of 129K applications spanning over three years. Our evaluation confirms that earlier published results are biased, while also revealing counter-intuitive performance and showing that appropriate tuning can lead to significant improvements.

Connected through hyperedge HE-1 (induced by source paper "on model evaluation under non constant class imbalance"):
  with members:
    - "deep residual learning for image recognition"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-2 (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"
    - "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"
    - "explaining black box android malware detection"

Connected through hyperedge HE-3 (induced by source paper "detecting and characterizing lateral phishing at scale"):
  with members:
    - "reviewer integration and performance measurement for malware detection"

Connected through hyperedge HE-4 (induced by source paper "eight years of rider measurement in the android malware ecosystem evolution and lessons learned"):
  with members:
    - "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"
    - "reviewer integration and performance measurement for malware detection"
    - "an investigation into the use of common libraries in android apps"

Connected through hyperedge HE-5 (induced by source paper "mamadroid detecting android malware by building markov chains of behavioral models extended version"):
  with members:
    - "a family of droids android malware detection via behavioral modeling static vs dynamic analysis"
    - "automated poisoning attacks and defenses in malware detection systems an adversarial machine learning approach"
    - "mamadroid detecting android malware by building markov chains of behavioral models"
    - "automated synthesis of semantic malware signatures using maximum satisfiability"

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)