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: "adversarial text generation via feature mover s distance"
  Abstract: Generative adversarial networks (GANs) have achieved significant success in generating real-valued data. However, the discrete nature of text hinders the application of GAN to text-generation tasks. Instead of using the standard GAN objective, we propose to improve text-generation GAN via a novel approach inspired by optimal transport. Specifically, we consider matching the latent feature distributions of real and synthetic sentences using a novel metric, termed the feature-mover's distance (FMD). This formulation leads to a highly discriminative critic and easy-to-optimize objective, overcoming the mode-collapsing and brittle-training problems in existing methods. Extensive experiments are conducted on a variety of tasks to evaluate the proposed model empirically, including unconditional text generation, style transfer from non-parallel text, and unsupervised cipher cracking. The proposed model yields superior performance, demonstrating wide applicability and effectiveness.

Connected through hyperedge HE-1 (induced by source paper "learning by semantic similarity makes abstractive summarization better"):
  with members:
    - "prophetnet predicting future n gram for sequence to sequence pre training"
    - "pegasus pre training with extracted gap sentences for abstractive summarization"
    - "summary level training of sentence rewriting for abstractive summarization"
    - "text summarization with pretrained encoders"
    - "roberta a robustly optimized bert pretraining approach"
    - "bart denoising sequence to sequence pre training for natural language generation translation and comprehension"
    - "better rewards yield better summaries learning to summarise without references"
    - "bottom up abstractive summarization"

Connected through hyperedge HE-2 (induced by source paper "defense against adversarial attacks using feature scattering based adversarial training"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "joint adversarial training incorporating both spatial and pixel attacks"
    - "towards adversarially robust object detection"
    - "defense against adversarial images using web scale nearest neighbor search"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "adversarial patch"
    - "feature denoising for improving adversarial robustness"
    - "adversarial logit pairing"

Connected through hyperedge HE-3 (induced by source paper "adversarial learning of deepfakes in accounting"):
  with members:
    - "detection of accounting anomalies in the latent space using adversarial autoencoder neural networks"
    - "text based editing of talking head video"
    - "did you hear that adversarial examples against automatic speech recognition"
    - "voice conversion with conditional samplernn"
    - "adversarial attacks and defences a survey"
    - "rsgan face swapping and editing using face and hair representation in latent spaces"
    - "everybody dance now"
    - "threat of adversarial attacks on deep learning in computer vision a survey"

Connected through hyperedge HE-4 (induced by source paper "improving textual network embedding with global attention via optimal transport"):
  with members:
    - "improving sequence to sequence learning via optimal transport"
    - "gromov wasserstein alignment of word embedding spaces"
    - "soft layer specific multi task summarization with entailment and question generation"
    - "improved semantic aware network embedding with fine grained word alignment"
    - "joint embedding of words and labels for text classification"
    - "diffusion maps for textual network embedding"
    - "stochastic answer networks for natural language inference"
    - "network representation learning a survey"

Connected through hyperedge HE-5 (induced by source paper "catgan category aware generative adversarial networks with hierarchical evolutionary learning for category text generation"):
  with members:
    - "on accurate evaluation of gans for language generation"
    - "the relativistic discriminator a key element missing from standard gan"
    - "evolutionary generative adversarial networks"
    - "relational recurrent neural networks"
    - "texygen a benchmarking platform for text generation models"
    - "language gans falling short"
    - "rebar low variance unbiased gradient estimates for discrete latent variable models"
    - "neural machine translation with gumbel greedy decoding"

Connected through hyperedge HE-6 (induced by source paper "gromov wasserstein learning for graph matching and node embedding"):
  with members:
    - "learning generative models across incomparable spaces"
    - "gromov wasserstein alignment of word embedding spaces"
    - "graph convolutional neural networks for web scale recommender systems"
    - "the gromov wasserstein distance between networks and stable network invariants"
    - "distilled wasserstein learning for word embedding and topic modeling"
    - "unsupervised alignment of embeddings with wasserstein procrustes"
    - "anonymous walk embeddings"
    - "triangular alignment tame a tensor based approach for higher order network alignment"

Connected through hyperedge HE-7 (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"
    - "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"
    - "toward controlled generation of text"

Connected through hyperedge HE-8 (induced by source paper "a multi language platform for generating algebraic mathematical word problems"):
  with members:
    - "neural text generation past present and beyond"
    - "automatic conditional generation of personalized social media short texts"
    - "an analysis of neural language modeling at multiple scales"
    - "salsa text self attentive latent space based adversarial text generation"
    - "language gans falling short"
    - "on the state of the art of evaluation in neural language models"
    - "adversarial ranking for language generation"
    - "maximum likelihood augmented discrete generative adversarial 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.CL(Computation and Language)