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: "meade towards a malicious email attachment detection engine"
  Abstract: Malicious email attachments are a growing delivery vector for malware. While machine learning has been successfully applied to portable executable (PE) malware detection, we ask, can we extend similar approaches to detect malware across heterogeneous file types commonly found in email attachments? In this paper, we explore the feasibility of applying machine learning as a static countermeasure to detect several types of malicious email attachments including Microsoft Office documents and Zip archives. To this end, we collected a dataset of over 5 million malicious/benign Microsoft Office documents from VirusTotal for evaluation as well as a dataset of benign Microsoft Office documents from the Common Crawl corpus, which we use to provide more realistic estimates of thresholds for false positive rates on in-the-wild data. We also collected a dataset of approximately 500k malicious/benign Zip archives, which we scraped using the VirusTotal service, on which we performed a separate evaluation. We analyze predictive performance of several classifiers on each of the VirusTotal datasets using a 70/30 train/test split on first seen time, evaluating feature and classifier types that have been applied successfully in commercial antimalware products and R&D contexts. Using deep neural networks and gradient boosted decision trees, we are able to obtain ROC curves with > 0.99 AUC on both Microsoft Office document and Zip archive datasets. Discussion of deployment viability in various antimalware contexts is provided.

Connected through hyperedge HE-1 (induced by source paper "automatic malware description via attribute tagging and similarity embedding"):
  with members:
    - "aloha auxiliary loss optimization for hypothesis augmentation"
    - "a deep learning approach to fast format agnostic detection of malicious web content"
    - "the effects of noisy labels on deep convolutional neural networks for music tagging"
    - "facial attributes accuracy and adversarial robustness"
    - "a survey of stealth malware attacks mitigation measures and steps toward autonomous open world solutions"
    - "fast zero shot image tagging"
    - "are facial attributes adversarially robust"
    - "automatic tagging using deep convolutional neural networks"

Connected through hyperedge HE-2 (induced by source paper "learning from context exploiting and interpreting file path information for better malware detection"):
  with members:
    - "aloha auxiliary loss optimization for hypothesis augmentation"
    - "ember an open dataset for training static pe malware machine learning models"
    - "a deep learning approach to fast format agnostic detection of malicious web content"
    - "microsoft malware classification challenge"
    - "expose a character level convolutional neural network with embeddings for detecting malicious urls file paths and registry keys"
    - "a multi view context aware approach to android malware detection and malicious code localization"
    - "in2i unsupervised multi image to image translation using generative adversarial networks"
    - "a survey of stealth malware attacks mitigation measures and steps toward autonomous open world solutions"

Connected through hyperedge HE-3 (induced by source paper "aloha auxiliary loss optimization for hypothesis augmentation"):
  with members:
    - "a deep learning approach to fast format agnostic detection of malicious web content"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a survey of stealth malware attacks mitigation measures and steps toward autonomous open world solutions"
    - "hyperface a deep multi task learning framework for face detection landmark localization pose estimation and gender recognition"
    - "moon a mixed objective optimization network for the recognition of facial attributes"
    - "deep neural network based malware detection using two dimensional binary program features"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "semi supervised learning with ladder networks"

Connected through hyperedge HE-4 (induced by source paper "principled uncertainty estimation for deep neural networks"):
  with members:
    - "neural processes"
    - "conditional neural processes"
    - "emnist an extension of mnist to handwritten letters"
    - "a survey of stealth malware attacks mitigation measures and steps toward autonomous open world solutions"
    - "density estimation using real nvp"
    - "fitted learning models with awareness of their limits"
    - "towards open set deep networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

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)