human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "snippext semi supervised opinion mining with augmented data"):
  Abstract: Online services are interested in solutions to opinion mining, which is the problem of extracting aspects, opinions, and sentiments from text. One method to mine opinions is to leverage the recent success of pre-trained language models which can be fine-tuned to obtain high-quality extractions from reviews. However, fine-tuning language models still requires a non-trivial amount of training data. In this paper, we study the problem of how to significantly reduce the amount of labeled training data required in fine-tuning language models for opinion mining. We describe Snippext, an opinion mining system developed over a language model that is fine-tuned through semi-supervised learning with augmented data. A novelty of Snippext is its clever use of a two-prong approach to achieve state-of-the-art (SOTA) performance with little labeled training data through: (1) data augmentation to automatically generate more labeled training data from existing ones, and (2) a semi-supervised learning technique to leverage the massive amount of unlabeled data in addition to the (limited amount of) labeled data. We show with extensive experiments that Snippext performs comparably and can even exceed previous SOTA results on several opinion mining tasks with only half the training data required. Furthermore, it achieves new SOTA results when all training data are leveraged. By comparison to a baseline pipeline, we found that Snippext extracts significantly more fine-grained opinions which enable new opportunities of downstream applications.

Includes members:
  Member "biobert a pre trained biomedical language representation model for biomedical text mining":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the microsoft toolkit of multi task deep neural networks for natural language understanding")
  Member "voyageur an experiential travel search engine":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "subjective databases")
  Member "eda easy data augmentation techniques for boosting performance on text classification tasks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "variational hierarchical dialog autoencoder for dialogue state tracking data augmentation")
  Member "subjective databases":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "teddy a system for interactive review analysis")
  Member "utilizing bert for aspect based sentiment analysis via constructing auxiliary sentence":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "description based text classification with reinforcement learning")
  Member "augmenting data with mixup for sentence classification an empirical study":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "mixup regularization for region proposal based object detectors")
  Member "scibert a pretrained language model for scientific text":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "navigation based candidate expansion and pretrained language models for citation recommendation")
  Member "scibert pretrained contextualized embeddings for scientific text":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "navigation based candidate expansion and pretrained language models for citation recommendation")

Question: Please tell me which class the source paper 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)