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: "the case for being average a mediocrity approach to style masking and author obfuscation"
  Abstract: Users posting online expect to remain anonymous unless they have logged in, which is often needed for them to be able to discuss freely on various topics. Preserving the anonymity of a text's writer can be also important in some other contexts, e.g., in the case of witness protection or anonymity programs. However, each person has his/her own style of writing, which can be analyzed using stylometry, and as a result, the true identity of the author of a piece of text can be revealed even if s/he has tried to hide it. Thus, it could be helpful to design automatic tools that can help a person obfuscate his/her identity when writing text. In particular, here we propose an approach that changes the text, so that it is pushed towards average values for some general stylometric characteristics, thus making the use of these characteristics less discriminative. The approach consists of three main steps: first, we calculate the values for some popular stylometric metrics that can indicate authorship; then we apply various transformations to the text, so that these metrics are adjusted towards the average level, while preserving the semantics and the soundness of the text; and finally, we add random noise. This approach turned out to be very efficient, and yielded the best performance on the Author Obfuscation task at the PAN-2016 competition.

Connected through hyperedge HE-1 (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-2 (induced by source paper "style obfuscation by invariance"):
  with members:
    - "when coding style survives compilation de anonymizing programmers from executable binaries"
    - "cross lingual distillation for text classification"
    - "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-3 (induced by source paper "generalised differential privacy for text document processing"):
  with members:
    - "privacy preserving neural representations of text"
    - "authorship verification based on compression models"
    - "learning differentially private recurrent language models"
    - "a comparison of neural models for word ordering"
    - "character level and multi channel convolutional neural networks for large scale authorship attribution"
    - "c sanitized a privacy model for document redaction and sanitization"
    - "geo indistinguishability differential privacy for location based systems"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-4 (induced by source paper "a 4 nt author attribute anonymity by adversarial training of neural machine translation"):
  with members:
    - "style transfer from non parallel text by cross alignment"
    - "sequence to sequence models can directly translate foreign speech"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "speaking the same language matching machine to human captions by adversarial training"
    - "adversarial learning for neural dialogue generation"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improving neural machine translation with conditional sequence generative adversarial nets"
    - "seqgan sequence generative adversarial nets with policy gradient"

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.CL(Computation and Language)