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: "benchmarking crimes an emerging threat in systems security"
  Abstract: Properly benchmarking a system is a difficult and intricate task. Unfortunately, even a seemingly innocuous benchmarking mistake can compromise the guarantees provided by a given systems security defense and also put its reproducibility and comparability at risk. This threat is particularly insidious as it is generally not a result of malice and can easily go undetected by both authors and reviewers. Moreover, as modern defenses often trade off security for performance in an attempt to find an ideal design point in the performance-security space, the damage caused by benchmarking mistakes is increasingly worrisome. #R##N#To analyze the magnitude of the phenomenon, we identify a set of 22 "benchmarking crimes" that threaten the validity of systems security evaluations and perform a survey of 50 defense papers published in top venues. To ensure the validity of our results, we perform the complete survey twice, with two independent readers. We find only a very small number of disagreements between readers, showing that our assessment of benchmarking crimes is highly reproducible. #R##N#We show that benchmarking crimes are widespread even in papers published at tier-1 venues. We find that tier-1 papers commit an average of five benchmarking crimes and we find only a single paper in our sample that committed no benchmarking crimes. Moreover, we find that the scale of the problem is constant over time, suggesting that the community is not yet addressing it despite the problem being now more relevant than ever. This threatens the scientific process, which relies on reproducibility and comparability to ensure that published research advances the state of the art. We hope to raise awareness of these issues and provide recommendations to improve benchmarking quality and safeguard the scientific process in our community.

Connected through hyperedge HE-1 (induced by source paper "unicorn runtime provenance based detector for advanced persistent threats"):
  with members:
    - "poirot aligning attack behavior with kernel audit records for cyber threat hunting"
    - "runtime analysis of whole system provenance"
    - "holmes real time apt detection through correlation of suspicious information flows"
    - "sleuth real time attack scenario reconstruction from cots audit data"
    - "saql a stream based query system for real time abnormal system behavior detection"
    - "how powerful are graph neural networks"
    - "frappuccino fault detection through runtime analysis of provenance"
    - "practical whole system provenance capture"

Connected through hyperedge HE-2 (induced by source paper "the dagstuhl beginners guide to reproducibility for experimental networking research"):
  with members:
    - "a long way to the top significance structure and stability of internet top lists"
    - "towards a rigorous methodology for measuring adoption of rpki route validation and filtering"

Connected through hyperedge HE-3 (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"
    - "mamadroid detecting android malware by building markov chains of behavioral models"
    - "why should i trust you explaining the predictions of any classifier"
    - "reviewer integration and performance measurement for malware detection"
    - "learning when training data are costly the effect of class distribution on tree induction"

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)