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: "exploiting latent attack semantics for intelligent malware detection"
  Abstract: Behavioral malware detectors promise to expose previously unknown malware and are an important security primitive. However, even the best behavioral detectors suffer from high false positives and negatives. In this paper, we address the challenge of aggregating weak per-device behavioral detectors in noisy communities (i.e., ones that produce alerts at unpredictable rates) into an accurate and robust global anomaly detector (GD). #R##N#Our system - Shape GD - combines two insights: Structural: actions such as visiting a website (waterhole attack) or membership in a shared email thread (phishing attack) by nodes correlate well with malware spread, and create dynamic neighborhoods of nodes that were exposed to the same attack vector; and Statistical: feature vectors corresponding to true and false positives of local detectors have markedly different conditional distributions. We use neighborhoods to amplify the transient low-dimensional structure that is latent in high-dimensional feature vectors - but neighborhoods vary unpredictably, and we use shape to extract robust neighborhood-level features that identify infected neighborhoods. #R##N#Unlike prior works that aggregate local detectors' alert bitstreams or cluster the feature vectors, Shape GD analyzes the feature vectors that led to local alerts (alert-FVs) to separate true and false positives. Shape GD first filters these alert-FVs into neighborhoods and efficiently maps a neighborhood's alert-FVs' statistical shapes into a scalar score. Shape GD then acts as a neighborhood level anomaly detector - training on benign program traces to learn the ShapeScore of false positive neighborhoods, and classifying neighborhoods with anomalous ShapeScores as malicious. #R##N#Shape GD detects malware early (~100 infected nodes in a ~100K node system for waterhole and ~10 of 1000 for phishing) and robustly (with ~100% global TP and ~1% global FP rates).

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)