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 4 nt author attribute anonymity by adversarial training of neural machine translation"
  Abstract: Text-based analysis methods allow to reveal privacy relevant author attributes such as gender, age and identify of the text's author. Such methods can compromise the privacy of an anonymous author even when the author tries to remove privacy sensitive content. In this paper, we propose an automatic method, called Adversarial Author Attribute Anonymity Neural Translation ($A^4NT$), to combat such text-based adversaries. We combine sequence-to-sequence language models used in machine translation and generative adversarial networks to obfuscate author attributes. Unlike machine translation techniques which need paired data, our method can be trained on unpaired corpora of text containing different authors. Importantly, we propose and evaluate techniques to impose constraints on our $A^4NT$ to preserve the semantics of the input text. $A^4NT$ learns to make minimal changes to the input text to successfully fool author attribute classifiers, while aiming to maintain the meaning of the input. We show through experiments on two different datasets and three settings that our proposed method is effective in fooling the author attribute classifiers and thereby improving the anonymity of authors.

Connected through hyperedge HE-1 (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"
    - "ember an open dataset for training static pe malware machine learning models"
    - "feature denoising for improving adversarial robustness"
    - "on the suitability of l_p norms for creating and preventing adversarial examples"

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 "text analysis in adversarial settings does deception leave a stylistic trace"):
  with members:
    - "hunting for troll comments in news community forums"
    - "all you need is love evading hate speech detection"
    - "a survey on natural language processing for fake news detection"
    - "style transfer through back translation"
    - "fine tuned language models for text classification"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "dear sir or madam may i introduce the yafc corpus corpus benchmarks and metrics for formality style transfer"
    - "stay on topic generating context specific fake restaurant reviews"

Connected through hyperedge HE-4 (induced by source paper "eat2seq a generic framework for controlled sentence transformation without task specific training"):
  with members:
    - "gromov wasserstein alignment of word embedding spaces"
    - "graph to sequence learning using gated graph neural networks"
    - "style transfer through back translation"
    - "a graph to sequence model for amr to text generation"
    - "a robust self learning method for fully unsupervised cross lingual mappings of word embeddings"
    - "dear sir or madam may i introduce the yafc corpus corpus benchmarks and metrics for formality style transfer"
    - "phrase based neural unsupervised machine translation"
    - "universal semantic parsing"

Connected through hyperedge HE-5 (induced by source paper "er ae differentially private text generation for authorship anonymization"):
  with members:
    - "author obfuscation using generalised differential privacy"
    - "unsupervised text style transfer using language models as discriminators"
    - "adversarial text generation via feature mover s distance"
    - "universal sentence encoder"
    - "content preserving text generation with attribute controls"
    - "a dataset of peer reviews peerread collection insights and nlp applications"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "syntf synthetic and differentially private term frequency vectors for privacy preserving text mining"

Connected through hyperedge HE-6 (induced by source paper "the daunting task of real world textual style transfer auto evaluation"):
  with members:
    - "learning the difference that makes a difference with counterfactually augmented data"
    - "evaluating style transfer for text"
    - "unsupervised text style transfer using language models as discriminators"
    - "learning sentiment memories for sentiment modification without parallel data"
    - "examining gender and race bias in two hundred sentiment analysis systems"
    - "delete retrieve generate a simple approach to sentiment and style transfer"
    - "learning criteria and evaluation metrics for textual transfer between non parallel corpora"
    - "style transfer through back translation"

Connected through hyperedge HE-7 (induced by source paper "unsupervised evaluation metrics and learning criteria for non parallel textual transfer"):
  with members:
    - "evaluating style transfer for text"
    - "unsupervised text style transfer using language models as discriminators"
    - "delete retrieve generate a simple approach to sentiment and style transfer"
    - "approximate ranking from pairwise comparisons"
    - "style transfer through back translation"
    - "content preserving text generation with attribute controls"
    - "fighting offensive language on social media with unsupervised text style transfer"
    - "dear sir or madam may i introduce the yafc corpus corpus benchmarks and metrics for formality style transfer"

Connected through hyperedge HE-8 (induced by source paper "understanding and controlling user linkability in decentralized learning"):
  with members:
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "imagenet training in minutes"
    - "towards a visual privacy advisor understanding and predicting privacy risks in images"
    - "knock knock who s there membership inference on aggregate location data"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "connecting pixels to privacy and utility automatic redaction of private information in images"
    - "learning differentially private recurrent language models"
    - "privacy preserving visual learning using doubly permuted homomorphic encryption"

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)