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: "when coding style survives compilation de anonymizing programmers from executable binaries"
  Abstract: The ability to identify authors of computer programs based on their coding style is a direct threat to the privacy and anonymity of programmers. While recent work found that source code can be attributed to authors with high accuracy, attribution of executable binaries appears to be much more difficult. Many distinguishing features present in source code, e.g. variable names, are removed in the compilation process, and compiler optimization may alter the structure of a program, further obscuring features that are known to be useful in determining authorship. We examine programmer de-anonymization from the standpoint of machine learning, using a novel set of features that include ones obtained by decompiling the executable binary to source code. We adapt a powerful set of techniques from the domain of source code authorship attribution along with stylistic representations embedded in assembly, resulting in successful de-anonymization of a large set of programmers. #R##N#We evaluate our approach on data from the Google Code Jam, obtaining attribution accuracy of up to 96% with 100 and 83% with 600 candidate programmers. We present an executable binary authorship attribution approach, for the first time, that is robust to basic obfuscations, a range of compiler optimization settings, and binaries that have been stripped of their symbol tables. We perform programmer de-anonymization using both obfuscated binaries, and real-world code found "in the wild" in single-author GitHub repositories and the recently leaked Nulled.IO hacker forum. We show that programmers who would like to remain anonymous need to take extreme countermeasures to protect their privacy.

Connected through hyperedge HE-1 (induced by source paper "parallelizing binary code analysis"):
  with members:
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-2 (induced by source paper "defending against machine learning based inference attacks via adversarial examples opportunities and challenges"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "misleading authorship attribution of source code using adversarial learning"
    - "mockingbird defending against deep learning based website fingerprinting attacks with adversarial traces"
    - "tagvisor a privacy advisor for sharing hashtags"
    - "generative adversarial privacy"
    - "poisoning attacks to graph based recommender systems"
    - "graph based security and privacy analytics via collective classification with joint weight learning and propagation"
    - "adversarial binaries for authorship identification"

Connected through hyperedge HE-3 (induced by source paper "memguard defending against black box membership inference attacks via adversarial examples"):
  with members:
    - "defending against machine learning based inference attacks via adversarial examples opportunities and challenges"
    - "differentially private model publishing for deep learning"
    - "misleading authorship attribution of source code using adversarial learning"
    - "evaluating differentially private machine learning in practice"
    - "under the hood of membership inference attacks on aggregate location time series"
    - "updates leak data set inference and reconstruction attacks in online learning"
    - "exploiting unintended feature leakage in collaborative learning"
    - "tagvisor a privacy advisor for sharing hashtags"

Connected through hyperedge HE-4 (induced by source paper "misleading authorship attribution of source code using adversarial learning"):
  with members:
    - "textbugger generating adversarial text against real world applications"
    - "generating natural language adversarial examples"
    - "sok security and privacy in machine learning"
    - "practical black box attacks against machine learning"
    - "stealing machine learning models via prediction apis"
    - "towards evaluating the robustness of neural networks"
    - "the limitations of deep learning in adversarial settings"
    - "evasion attacks against machine learning at test time"

Connected through hyperedge HE-5 (induced by source paper "style obfuscation by invariance"):
  with members:
    - "cross lingual distillation for text classification"
    - "the case for being average a mediocrity approach to style masking and author obfuscation"
    - "a 4 nt author attribute anonymity by adversarial training of neural machine translation"
    - "show adapt and tell adversarial training of cross domain image captioner"
    - "controlling linguistic style aspects in neural language generation"
    - "controllable invariance through adversarial feature learning"
    - "adversarial multi task learning for text classification"
    - "depression and self harm risk assessment in online forums"

Connected through hyperedge HE-6 (induced by source paper "adversarial binaries for authorship identification"):
  with members:
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "dolphinattack inaudible voice commands"
    - "transferability in machine learning from phenomena to black box attacks using adversarial samples"
    - "towards evaluating the robustness of neural networks"
    - "the limitations of deep learning in adversarial settings"
    - "adam a method for stochastic optimization"
    - "evasion attacks against machine learning at test time"
    - "intriguing properties of neural networks"

Connected through hyperedge HE-7 (induced by source paper "end to end deep neural networks and transfer learning for automatic analysis of nation state malware"):
  with members:
    - "deepapt nation state apt attribution using end to end deep neural networks"
    - "on the feasibility of malware authorship attribution"
    - "deepsign deep learning for automatic malware signature generation and classification"
    - "novel feature extraction selection and fusion for effective malware family classification"
    - "decaf a deep convolutional activation feature for generic visual recognition"
    - "visualizing and understanding convolutional networks"
    - "representation learning a review and new perspectives"
    - "natural language processing almost from scratch"

Connected through hyperedge HE-8 (induced by source paper "learning fast and slow propedeutica for real time malware detection"):
  with members:
    - "mean birds detecting aggression and bullying on twitter"
    - "adversarial perturbations against deep neural networks for malware classification"
    - "mamadroid detecting android malware by building markov chains of behavioral models"
    - "xgboost a scalable tree boosting system"
    - "towards evaluating the robustness of neural networks"
    - "applying bag of system calls for anomalous behavior detection of applications in linux containers"
    - "deep neural network based malware detection using two dimensional binary program features"
    - "distillation as a defense to adversarial perturbations against deep neural networks"

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)