human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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).

Includes members:
  Member "practical black box attacks against machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "adversarial ranking attack and defense")
  Member "reviewer integration and performance measurement for malware detection":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "intriguing properties of adversarial ml attacks in the problem space")
  Member "evolution of conversations in the age of email overload":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "characterizing reading time on enterprise emails")
  Member "detection of early stage enterprise infection by mining large scale log data":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "attacking graph based classification via manipulating the graph structure")
  Member "a tutorial on spectral clustering":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "approximating spectral clustering via sampling a review")

Question: Please tell me which class the source paper 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)