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: "expose a character level convolutional neural network with embeddings for detecting malicious urls file paths and registry keys"
  Abstract: For years security machine learning research has promised to obviate the need for signature based detection by automatically learning to detect indicators of attack. Unfortunately, this vision hasn't come to fruition: in fact, developing and maintaining today's security machine learning systems can require engineering resources that are comparable to that of signature-based detection systems, due in part to the need to develop and continuously tune the "features" these machine learning systems look at as attacks evolve. Deep learning, a subfield of machine learning, promises to change this by operating on raw input signals and automating the process of feature design and extraction. In this paper we propose the eXpose neural network, which uses a deep learning approach we have developed to take generic, raw short character strings as input (a common case for security inputs, which include artifacts like potentially malicious URLs, file paths, named pipes, named mutexes, and registry keys), and learns to simultaneously extract features and classify using character-level embeddings and convolutional neural network. In addition to completely automating the feature design and extraction process, eXpose outperforms manual feature extraction based baselines on all of the intrusion detection problems we tested it on, yielding a 5%-10% detection rate gain at 0.1% false positive rate compared to these baselines.

Connected through hyperedge HE-1 (induced by source paper "nested multiple instance learning in modelling of http network traffic"):
  with members:
    - "approximation capability of neural networks on spaces of probability measures and tree structured domains"
    - "discriminative models for multi instance problems with tree structure"
    - "predicting domain generation algorithms with long short term memory networks"
    - "using neural network formalism to solve multiple instance problems"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "julia a fresh approach to numerical computing"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-2 (induced by source paper "inline detection of dga domains using side information"):
  with members:
    - "maskdga a black box evasion technique against dga classifiers and adversarial defenses"
    - "using lexical features for malicious url detection a machine learning approach"
    - "charbot a simple and effective method for evading dga classifiers"
    - "detecting dga domains with recurrent neural networks and side information"
    - "inline detection of domain generation algorithms with context sensitive word embeddings"
    - "predicting domain generation algorithms with long short term memory networks"
    - "deepdga adversarially tuned domain generation and detection"

Connected through hyperedge HE-3 (induced by source paper "procedural noise adversarial examples for black box attacks on deep convolutional networks"):
  with members:
    - "on evaluating adversarial robustness"
    - "spatially transformed adversarial examples"
    - "yolov3 an incremental improvement"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "generating adversarial examples with adversarial networks"
    - "sok security and privacy in machine learning"
    - "defense against the dark arts an overview of adversarial example security research and future research directions"
    - "audio adversarial examples targeted attacks on speech to text"

Connected through hyperedge HE-4 (induced by source paper "maskdga a black box evasion technique against dga classifiers and adversarial defenses"):
  with members:
    - "detecting dga domains with recurrent neural networks and side information"
    - "inline detection of domain generation algorithms with context sensitive word embeddings"
    - "black box generation of adversarial text sequences to evade deep learning classifiers"
    - "exploring adversarial examples in malware detection"
    - "detection of malicious and low throughput data exfiltration over the dns protocol"
    - "hotflip white box adversarial examples for text classification"
    - "generating adversarial malware examples for black box attacks based on gan"
    - "generic black box end to end attack against state of the art api call based malware classifiers"

Connected through hyperedge HE-5 (induced by source paper "amsi based detection of malicious powershell code using contextual embeddings"):
  with members:
    - "attack2vec leveraging temporal word embeddings to understand the evolution of cyberattacks"
    - "ast based deep learning for detecting malicious powershell"
    - "neural classification of malicious scripts a study with javascript and vbscript"
    - "deep contextualized word representations"
    - "detecting malicious powershell commands using deep neural networks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "enriching word vectors with subword information"

Connected through hyperedge HE-6 (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"
    - "a deep learning approach to fast format agnostic detection of malicious web content"
    - "microsoft malware classification challenge"
    - "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-7 (induced by source paper "charbot a simple and effective method for evading dga classifiers"):
  with members:
    - "maskdga a black box evasion technique against dga classifiers and adversarial defenses"
    - "inline detection of domain generation algorithms with context sensitive word embeddings"
    - "semidefinite relaxations for certifying robustness to adversarial examples"
    - "adversarial texts with gradient methods"
    - "passgan a deep learning approach for password guessing"
    - "generating adversarial malware examples for black box attacks based on gan"
    - "automatic detection of malware generated domains with recurrent neural models"
    - "predicting domain generation algorithms with long short term memory networks"

Connected through hyperedge HE-8 (induced by source paper "meade towards a malicious email attachment detection engine"):
  with members:
    - "ember an open dataset for training static pe malware machine learning models"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "open set intrusion recognition for fine grained attack categorization"
    - "facial attributes accuracy and adversarial robustness"
    - "a survey of stealth malware attacks mitigation measures and steps toward autonomous open world solutions"
    - "xgboost a scalable tree boosting system"
    - "adversarial diversity and hard positive generation"
    - "are facial attributes adversarially robust"

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)