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: "automated crowdturfing attacks and defenses in online review systems"
  Abstract: Malicious crowdsourcing forums are gaining traction as sources of spreading misinformation online, but are limited by the costs of hiring and managing human workers. In this paper, we identify a new class of attacks that leverage deep learning language models (Recurrent Neural Networks or RNNs) to automate the generation of fake online reviews for products and services. Not only are these attacks cheap and therefore more scalable, but they can control rate of content output to eliminate the signature burstiness that makes crowdsourced campaigns easy to detect. #R##N#Using Yelp reviews as an example platform, we show how a two phased review generation and customization attack can produce reviews that are indistinguishable by state-of-the-art statistical detectors. We conduct a survey-based user study to show these reviews not only evade human detection, but also score high on "usefulness" metrics by users. Finally, we develop novel automated defenses against these attacks, by leveraging the lossy transformation introduced by the RNN training and generation cycle. We consider countermeasures against our mechanisms, show that they produce unattractive cost-benefit tradeoffs for attackers, and that they can be further curtailed by simple constraints imposed by online service providers.

Connected through hyperedge HE-1 (induced by source paper "regularisation can mitigate poisoning attacks a novel analysis based on multiobjective bilevel optimisation"):
  with members:
    - "poisoning attacks with generative adversarial nets"
    - "sever a robust meta algorithm for stochastic optimization"
    - "is feature selection secure against training data poisoning"
    - "poison frogs targeted clean label poisoning attacks on neural networks"
    - "certified defenses for data poisoning attacks"
    - "towards poisoning of deep learning algorithms with back gradient optimization"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-2 (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-3 (induced by source paper "updates leak data set inference and reconstruction attacks in online learning"):
  with members:
    - "memguard defending against black box membership inference attacks via adversarial examples"
    - "disparate vulnerability on the unfairness of privacy attacks against machine learning"
    - "diversity sensitive conditional generative adversarial networks"
    - "under the hood of membership inference attacks on aggregate location time series"
    - "how to prove your model belongs to you a blind watermark based framework to protect intellectual property of dnn"
    - "exploiting unintended feature leakage in collaborative learning"
    - "knockoff nets stealing functionality of black box models"
    - "tagvisor a privacy advisor for sharing hashtags"

Connected through hyperedge HE-4 (induced by source paper "learning from fact checkers analysis and generation of fact checking language"):
  with members:
    - "homogeneity based transmissive process to model true and false news in social networks"
    - "the rise of guardians fact checking url recommendation to combat fake news"
    - "declare debunking fake news and false claims using evidence aware deep learning"
    - "fever a large scale dataset for fact extraction and verification"
    - "variational memory encoder decoder"
    - "fact checking in community forums"
    - "automated hate speech detection and the problem of offensive language"
    - "liar liar pants on fire a new benchmark dataset for fake news detection"

Connected through hyperedge HE-5 (induced by source paper "persona aware tips generation"):
  with members:
    - "skeleton to response dialogue generation guided by retrieval memory"
    - "actor critic based training framework for abstractive summarization"
    - "quase sequence editing under quantifiable guidance"
    - "toward controlled generation of text"
    - "adversarial variational bayes unifying variational autoencoders and generative adversarial networks"
    - "deep recurrent generative decoder for abstractive text summarization"
    - "neural rating regression with abstractive tips generation for recommendation"
    - "learning discourse level diversity for neural dialog models using conditional variational autoencoders"

Connected through hyperedge HE-6 (induced by source paper "towards controllable and personalized review generation"):
  with members:
    - "self attention with relative position representations"
    - "texygen a benchmarking platform for text generation models"
    - "learning to generate reviews and discovering sentiment"
    - "toward controlled generation of text"
    - "adversarial ranking for language generation"
    - "improved training of wasserstein gans"
    - "long text generation via adversarial training with leaked information"
    - "sequence to sequence learning as beam search optimization"

Connected through hyperedge HE-7 (induced by source paper "teevil identity lease via trusted execution environments"):
  with members:
    - "stay on topic generating context specific fake restaurant reviews"
    - "fake news detection on social media a data mining perspective"
    - "software grand exposure sgx cache attacks are practical"
    - "rollback and forking detection for trusted execution environments using lightweight collective memory"
    - "inferring fine grained control flow inside sgx enclaves with branch shadowing"
    - "debunking in a world of tribes"
    - "can cascades be predicted"
    - "viral misinformation the role of homophily and polarization"

Connected through hyperedge HE-8 (induced by source paper "fraudjudger real world data oriented fraud detection on digital payment platforms"):
  with members:
    - "online human bot interactions detection estimation and characterization"
    - "seqgan sequence generative adversarial nets with policy gradient"
    - "smote synthetic minority over sampling technique"

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)