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: "ember an open dataset for training static pe malware machine learning models"
  Abstract: This paper describes EMBER: a labeled benchmark dataset for training machine learning models to statically detect malicious Windows portable executable files. The dataset includes features extracted from 1.1M binary files: 900K training samples (300K malicious, 300K benign, 300K unlabeled) and 200K test samples (100K malicious, 100K benign). To accompany the dataset, we also release open source code for extracting features from additional binaries so that additional sample features can be appended to the dataset. This dataset fills a void in the information security machine learning community: a benign/malicious dataset that is large, open and general enough to cover several interesting use cases. We enumerate several use cases that we considered when structuring the dataset. Additionally, we demonstrate one use case wherein we compare a baseline gradient boosted decision tree model trained using LightGBM with default settings to MalConv, a recently published end-to-end (featureless) deep learning model for malware detection. Results show that even without hyper-parameter optimization, the baseline EMBER model outperforms MalConv. The authors hope that the dataset, code and baseline model provided by EMBER will help invigorate machine learning research for malware detection, in much the same way that benchmark datasets have advanced computer vision research.

Connected through hyperedge HE-1 (induced by source paper "automatic generation of adversarial examples for interpreting malware classifiers"):
  with members:
    - "understanding the efficacy reliability and resiliency of computer vision techniques for malware detection and future research directions"
    - "atmpa attacking machine learning based malware visualization detection methods via adversarial examples"
    - "on defending against label flipping attacks on malware detection systems"
    - "adversarial samples on android malware detection systems for iot systems"
    - "copycat practical adversarial attacks on visualization based malware detection"
    - "generation evaluation of adversarial examples for malware obfuscation"
    - "examining adversarial learning against graph based iot malware detection systems"
    - "explaining vulnerabilities of deep learning to adversarial malware binaries"

Connected through hyperedge HE-2 (induced by source paper "optimization guided binary diversification to mislead neural networks for malware detection"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "misleading authorship attribution of source code using adversarial learning"
    - "explaining vulnerabilities of deep learning to adversarial malware binaries"
    - "theoretically principled trade off between robustness and accuracy"
    - "counterstrike defending deep learning architectures against adversarial samples by langevin dynamics with supervised denoising autoencoder"
    - "feature denoising for improving adversarial robustness"
    - "on the suitability of l_p norms for creating and preventing adversarial examples"
    - "learning to evade static pe machine learning malware models via reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "malware evasion attack and defense"):
  with members:
    - "explaining vulnerabilities of deep learning to adversarial malware binaries"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "adversarial malware binaries evading deep learning for malware detection in executables"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "enhancing robustness of machine learning systems via data transformations"
    - "generating adversarial malware examples for black box attacks based on gan"
    - "synthesizing robust adversarial examples"

Connected through hyperedge HE-4 (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"
    - "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"
    - "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-5 (induced by source paper "towards a robust classifier an mdl based method for generating adversarial examples"):
  with members:
    - "adversarial deep learning for robust detection of binary encoded malware"
    - "exploring the space of black box attacks on deep neural networks"
    - "generating adversarial examples with adversarial networks"
    - "improving the adversarial robustness and interpretability of deep neural networks by regularizing their input gradients"
    - "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"
    - "generating natural adversarial examples"
    - "practical black box attacks against machine learning"

Connected through hyperedge HE-6 (induced by source paper "explaining vulnerabilities of deep learning to adversarial malware binaries"):
  with members:
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "adversarial malware binaries evading deep learning for malware detection in executables"
    - "axiomatic attribution for deep networks"
    - "adversarial perturbations against deep neural networks for malware classification"
    - "why should i trust you explaining the predictions of any classifier"
    - "towards evaluating the robustness of neural networks"
    - "deepsign deep learning for automatic malware signature generation and classification"
    - "deep neural network based malware detection using two dimensional binary program features"

Connected through hyperedge HE-7 (induced by source paper "neurlux dynamic malware analysis without feature engineering"):
  with members:
    - "early stage malware prediction using recurrent neural networks"
    - "attention is all you need"
    - "automated dynamic analysis of ransomware benefits limitations and use for detection"
    - "learning transferable features with deep adaptation networks"
    - "deep neural network based malware detection using two dimensional binary program features"
    - "a c lstm neural network for text classification"
    - "deep neural networks are easily fooled high confidence predictions for unrecognizable images"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-8 (induced by source paper "would a file by any other name seem as malicious"):
  with members:
    - "activation analysis of a byte based deep neural network for malware classification"
    - "learning from context exploiting and interpreting file path information for better malware detection"
    - "explaining vulnerabilities of deep learning to adversarial malware binaries"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "deep neural network based malware detection using two dimensional binary program features"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "text understanding from scratch"
    - "adam a method for stochastic optimization"

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)