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 "automatic malware description via attribute tagging and similarity embedding"):
  Abstract: With the rapid proliferation and increased sophistication of malicious software (malware), detection methods no longer rely only on manually generated signatures but have also incorporated more general approaches like machine learning detection. Although powerful for conviction of malicious artifacts, these methods do not produce any further information about the type of threat that has been detected neither allows for identifying relationships between malware samples. In this work, we address the information gap between machine learning and signature-based detection methods by learning a representation space for malware samples in which files with similar malicious behaviors appear close to each other. We do so by introducing a deep learning based tagging model trained to generate human-interpretable semantic descriptions of malicious software, which, at the same time provides potentially more useful and flexible information than malware family names. #R##N#We show that the malware descriptions generated with the proposed approach correctly identify more than 95% of eleven possible tag descriptions for a given sample, at a deployable false positive rate of 1% per tag. Furthermore, we use the learned representation space to introduce a similarity index between malware files, and empirically demonstrate using dynamic traces from files' execution, that is not only more effective at identifying samples from the same families, but also 32 times smaller than those based on raw feature vectors.

Includes members:
  Member "aloha auxiliary loss optimization for hypothesis augmentation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning from context exploiting and interpreting file path information for better malware detection")
  Member "meade towards a malicious email attachment detection engine":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning from context exploiting and interpreting file path information for better malware detection")
  Member "a deep learning approach to fast format agnostic detection of malicious web content":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning from context exploiting and interpreting file path information for better malware detection")
  Member "the effects of noisy labels on deep convolutional neural networks for music tagging":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "wasserstein adversarial regularization war on label noise")
  Member "facial attributes accuracy and adversarial robustness":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "unsupervised enhancement of soft biometric privacy with negative face recognition")
  Member "a survey of stealth malware attacks mitigation measures and steps toward autonomous open world solutions":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "machine learning in iot security current solutions and future challenges")
  Member "fast zero shot image tagging":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "captioning images with novel objects via online vocabulary expansion")
  Member "are facial attributes adversarially robust":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "privacynet semi adversarial networks for multi attribute face privacy")

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.LG(Machine Learning)