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: "deep neural network based malware detection using two dimensional binary program features"
  Abstract: Malware remains a serious problem for corporations, government agencies, and individuals, as attackers continue to use it as a tool to effect frequent and costly network intrusions. Machine learning holds the promise of automating the work required to detect newly discovered malware families, and could potentially learn generalizations about malware and benign software that support the detection of entirely new, unknown malware families. Unfortunately, few proposed machine learning based malware detection methods have achieved the low false positive rates required to deliver deployable detectors. #R##N#In this paper we a deep neural network malware classifier that achieves a usable detection rate at an extremely low false positive rate and scales to real world training example volumes on commodity hardware. Specifically, we show that our system achieves a 95% detection rate at 0.1% false positive rate (FPR), based on more than 400,000 software binaries sourced directly from our customers and internal malware databases. We achieve these results by directly learning on all binaries, without any filtering, unpacking, or manually separating binary files into categories. Further, we confirm our false positive rates directly on a live stream of files coming in from Invincea's deployed endpoint solution, provide an estimate of how many new binary files we expected to see a day on an enterprise network, and describe how that relates to the false positive rate and translates into an intuitive threat score. #R##N#Our results demonstrate that it is now feasible to quickly train and deploy a low resource, highly accurate machine learning classification model, with false positive rates that approach traditional labor intensive signature based methods, while also detecting previously unseen malware.

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 "analyzing accuracy loss in randomized smoothing defenses"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "certified adversarial robustness with additive noise"
    - "certified defenses against adversarial examples"
    - "efficient defenses against adversarial attacks"
    - "towards robust neural networks via random self ensemble"
    - "understanding deep learning requires rethinking generalization"
    - "towards evaluating the robustness of neural networks"
    - "robustness of classifiers from adversarial to random noise"

Connected through hyperedge HE-3 (induced by source paper "tools and techniques for malware detection and analysis"):
  with members:
    - "dl droid deep learning based android malware detection using real devices"
    - "mobile technology in healthcare environment security vulnerabilities and countermeasures"
    - "attacks and defenses in mobile ip modeling with stochastic game petri net"
    - "abusniff automatic detection and defenses against abusive facebook friends"
    - "robust and effective malware detection through quantitative data flow graph metrics"

Connected through hyperedge HE-4 (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-5 (induced by source paper "on training robust pdf malware classifiers"):
  with members:
    - "robust decision trees against adversarial examples"
    - "enhancing gradient based attacks with symbolic intervals"
    - "scaling provable adversarial defenses"
    - "adversarial attack on graph structured data"
    - "formal security analysis of neural networks using symbolic intervals"
    - "on the effectiveness of interval bound propagation for training verifiably robust models"
    - "a dual approach to scalable verification of deep networks"
    - "cost sensitive robustness against adversarial examples"

Connected through hyperedge HE-6 (induced by source paper "effectiveness of adversarial examples and defenses for malware classification"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "adversarial deep learning for robust detection of binary encoded malware"
    - "black box attacks against rnn based malware detection algorithms"
    - "attack and defense of dynamic analysis based adversarial neural malware classification models"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "hardening malware detection systems against cyber maneuvers an adversarial machine learning approach"
    - "improved training of wasserstein gans"
    - "generating adversarial malware examples for black box attacks based on gan"

Connected through hyperedge HE-7 (induced by source paper "rallying adversarial techniques against deep learning for network security"):
  with members:
    - "adversarial examples are a natural consequence of test error in noise"
    - "on evaluating adversarial robustness"
    - "spatially transformed adversarial examples"
    - "adversarial attacks and defences competition"
    - "adversarial vision challenge"
    - "kitsune an ensemble of autoencoders for online network intrusion detection"
    - "adversarial spheres"
    - "adversarial malware binaries evading deep learning for malware detection in executables"

Connected through hyperedge HE-8 (induced by source paper "i mad a novel interpretable malware detector using hierarchical transformer"):
  with members:
    - "interpretable neural networks for predicting mortality risk using multi modal electronic health records"
    - "mass masked sequence to sequence pre training for language generation"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "unified language model pre training for natural language understanding and generation"
    - "deep ehr a survey of recent advances in deep learning techniques for electronic health record ehr analysis"
    - "deep contextualized word representations"
    - "fine tuned language models for text classification"
    - "bert pre training of deep bidirectional transformers for language understanding"

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)