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: "a deep learning approach to fast format agnostic detection of malicious web content"
  Abstract: Malicious web content is a serious problem on the Internet today. In this paper we propose a deep learning approach to detecting malevolent web pages. While past work on web content detection has relied on syntactic parsing or on emulation of HTML and Javascript to extract features, our approach operates directly on a language-agnostic stream of tokens extracted directly from static HTML files with a simple regular expression. This makes it fast enough to operate in high-frequency data contexts like firewalls and web proxies, and allows it to avoid the attack surface exposure of complex parsing and emulation code. Unlike well-known approaches such as bag-of-words models, which ignore spatial information, our neural network examines content at hierarchical spatial scales, allowing our model to capture locality and yielding superior accuracy compared to bag-of-words baselines. Our proposed architecture achieves a 97.5% detection rate at a 0.1% false positive rate, and classifies small-batched web pages at a rate of over 100 per second on commodity hardware. The speed and accuracy of our approach makes it appropriate for deployment to endpoints, firewalls, and web proxies.

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"
    - "meade towards a malicious email attachment detection engine"
    - "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"
    - "meade towards a malicious email attachment detection engine"
    - "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:
    - "meade towards a malicious email attachment detection engine"
    - "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 "malicious url detection using machine learning a survey"):
  with members:
    - "online learning a comprehensive survey"
    - "urlnet learning a url representation with deep learning for malicious url detection"
    - "expose a character level convolutional neural network with embeddings for detecting malicious urls file paths and registry keys"
    - "online deep learning learning deep neural networks on the fly"
    - "gradient episodic memory for continual learning"
    - "identifying malicious web domains using machine learning techniques with online credibility and performance data"
    - "sol a library for scalable online learning algorithms"
    - "character level convolutional networks for text classification"

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)