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: "data noising as smoothing in neural network language models"
  Abstract: Data noising is an effective technique for regularizing neural network models. While noising is widely adopted in application domains such as vision and speech, commonly used noising primitives have not been developed for discrete sequence-level settings such as language modeling. In this paper, we derive a connection between input noising in neural network language models and smoothing in $n$-gram models. Using this connection, we draw upon ideas from smoothing to develop effective noising schemes. We demonstrate performance gains when applying the proposed schemes to language modeling and machine translation. Finally, we provide empirical analysis validating the relationship between noising and smoothing.

Connected through hyperedge HE-1 (induced by source paper "learning data manipulation for augmentation and weighting"):
  with members:
    - "eda easy data augmentation techniques for boosting performance on text classification tasks"
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "contextual augmentation data augmentation by words with paradigmatic relations"
    - "jointly optimize data augmentation and network training adversarial data augmentation in human pose estimation"
    - "on learning intrinsic rewards for policy gradient methods"
    - "not all samples are created equal deep learning with importance sampling"
    - "autoaugment learning augmentation policies from data"
    - "conditional bert contextual augmentation"

Connected through hyperedge HE-2 (induced by source paper "graphmix regularized training of graph neural networks for semi supervised learning"):
  with members:
    - "gmnn graph markov neural networks"
    - "graph u nets"
    - "mixmatch a holistic approach to semi supervised learning"
    - "graph adversarial training dynamically regularizing based on graph structure"
    - "batch virtual adversarial training for graph convolutional networks"
    - "graph neural networks a review of methods and applications"
    - "understanding and improving interpolation in autoencoders via an adversarial regularizer"
    - "dropblock a regularization method for convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "not enough data deep learning to the rescue"):
  with members:
    - "data augmentation using gans"
    - "eda easy data augmentation techniques for boosting performance on text classification tasks"
    - "improving short text classification through global augmentation methods"
    - "contextual augmentation data augmentation by words with paradigmatic relations"
    - "strong baselines for neural semi supervised learning under domain shift"
    - "deep contextualized word representations"
    - "conditional bert contextual augmentation"
    - "qanet combining local convolution with global self attention for reading comprehension"

Connected through hyperedge HE-4 (induced by source paper "soft contextual data augmentation for neural machine translation"):
  with members:
    - "contextual augmentation data augmentation by words with paradigmatic relations"
    - "switchout an efficient data augmentation algorithm for neural machine translation"
    - "autoaugment learning augmentation policies from data"
    - "conditional bert contextual augmentation"
    - "data augmentation for low resource neural machine translation"
    - "unsupervised machine translation using monolingual corpora only"
    - "attention is all you need"
    - "unsupervised neural machine translation"

Connected through hyperedge HE-5 (induced by source paper "bpe dropout simple and effective subword regularization"):
  with members:
    - "a call for prudent choice of subword merge operations in neural machine translation"
    - "revisiting character based neural machine translation with capacity and compression"
    - "learning to segment inputs for nmt favors character level processing"
    - "finding better subword segmentation for neural machine translation"
    - "unsupervised statistical machine translation"
    - "contextual augmentation data augmentation by words with paradigmatic relations"
    - "frage frequency agnostic word representation"
    - "subword regularization improving neural network translation models with multiple subword candidates"

Connected through hyperedge HE-6 (induced by source paper "eda easy data augmentation techniques for boosting performance on text classification tasks"):
  with members:
    - "low resource text classification with ulmfit and backtranslation"
    - "deep learning is robust to massive label noise"
    - "contextual augmentation data augmentation by words with paradigmatic relations"
    - "deep contextualized word representations"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "data augmentation for low resource neural machine translation"
    - "toward controlled generation of text"

Connected through hyperedge HE-7 (induced by source paper "integrating semantic knowledge to tackle zero shot text classification"):
  with members:
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "a large scale attribute dataset for zero shot learning"
    - "contextual augmentation data augmentation by words with paradigmatic relations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "data augmentation for low resource neural machine translation"
    - "train once test anywhere zero shot learning for text classification"
    - "zero shot learning a comprehensive evaluation of the good the bad and the ugly"
    - "i2t2i learning text to image synthesis with textual data augmentation"

Connected through hyperedge HE-8 (induced by source paper "imbalanced sentiment classification enhanced with discourse marker"):
  with members:
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "unpaired sentiment to sentiment translation a cycled reinforcement learning approach"
    - "data augmentation for low resource neural machine translation"
    - "toward controlled generation of text"
    - "dissent sentence representation learning from explicit discourse relations"
    - "adversarial examples for evaluating reading comprehension systems"
    - "aspect level sentiment classification with deep memory network"
    - "ups and downs modeling the visual evolution of fashion trends with one class collaborative filtering"

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.LG(Machine Learning)