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: "imat unsupervised text attribute transfer via iterative matching and translation"
  Abstract: Text attribute transfer aims to automatically rewrite sentences such that they possess certain linguistic attributes, while simultaneously preserving their semantic content. This task remains challenging due to a lack of supervised parallel data. Existing approaches try to explicitly disentangle content and attribute information, but this is difficult and often results in poor content-preservation and ungrammaticality. In contrast, we propose a simpler approach, Iterative Matching and Translation (IMaT), which: (1) constructs a pseudo-parallel corpus by aligning a subset of semantically similar sentences from the source and the target corpora; (2) applies a standard sequence-to-sequence model to learn the attribute transfer; (3) iteratively improves the learned transfer function by refining imperfections in the alignment. In sentiment modification and formality transfer tasks, our method outperforms complex state-of-the-art systems by a large margin. As an auxiliary contribution, we produce a publicly-available test set with human-generated transfer references.

Connected through hyperedge HE-1 (induced by source paper "is bert really robust a strong baseline for natural language attack on text classification and entailment"):
  with members:
    - "probing neural network comprehension of natural language arguments"
    - "pathologies of neural models make interpretations difficult"
    - "textbugger generating adversarial text against real world applications"
    - "black box generation of adversarial text sequences to evade deep learning classifiers"
    - "deep text classification can be fooled"
    - "generating natural language adversarial examples"
    - "deep contextualized word representations"
    - "universal sentence encoder"

Connected through hyperedge HE-2 (induced by source paper "style transformer unpaired text style transfer without disentangled latent representation"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "style transfer as unsupervised machine translation"
    - "disentangled representation learning for non parallel text style transfer"
    - "delete retrieve generate a simple approach to sentiment and style transfer"
    - "style transfer through back translation"
    - "shaped shared private encoder decoder for text style adaptation"
    - "fighting offensive language on social media with unsupervised text style transfer"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-3 (induced by source paper "encode tag realize high precision text editing"):
  with members:
    - "fine tune bert for extractive summarization"
    - "editnts an neural programmer interpreter model for sentence simplification through explicit editing"
    - "improving grammatical error correction via pre training a copy augmented architecture with unlabeled data"
    - "discofuse a large scale dataset for discourse based sentence fusion"
    - "step by step separating planning from realization in neural data to text generation"
    - "approaching neural grammatical error correction as a low resource machine translation task"
    - "learning neural templates for text generation"
    - "ranking sentences for extractive summarization with reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "semi supervised text style transfer cross projection in latent space"):
  with members:
    - "style transfer as unsupervised machine translation"
    - "disentangled representation learning for non parallel text style transfer"
    - "unsupervised stylish image description generation via domain layer norm"
    - "style transfer through back translation"
    - "content preserving text generation with attribute controls"
    - "difficulty controllable generation of reading comprehension questions"
    - "language style transfer from sentences with arbitrary unknown styles"
    - "dear sir or madam may i introduce the yafc corpus corpus benchmarks and metrics for formality style transfer"

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)