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: "subjective databases"
  Abstract: Online users are constantly seeking experiences, such as a hotel with clean rooms and a lively bar, or a restaurant for a romantic rendezvous. However, e-commerce search engines only support queries involving objective attributes such as location, price, and cuisine, and any experiential data is relegated to text reviews.  In order to support experiential queries, a database system needs to model subjective data and also be able to process queries where the user can express varied subjective experiences in words chosen by the user, in addition to specifying predicates involving objective attributes. This paper introduces Opine, a subjective database system that addresses these challenges. We introduce a data model for subjective databases. We describe how Opine translates subjective queries against the subjective database schema, which is done by matching the user query phrases to the underlying schema. We also show how the experiential conditions specified by the user can be combined and the results aggregated and ranked. We demonstrate that subjective databases satisfy user needs more effectively and accurately than alternative techniques through experiments with real data of hotel and restaurant reviews.

Connected through hyperedge HE-1 (induced by source paper "snippext semi supervised opinion mining with augmented data"):
  with members:
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "voyageur an experiential travel search engine"
    - "eda easy data augmentation techniques for boosting performance on text classification tasks"
    - "utilizing bert for aspect based sentiment analysis via constructing auxiliary sentence"
    - "augmenting data with mixup for sentence classification an empirical study"
    - "scibert a pretrained language model for scientific text"
    - "scibert pretrained contextualized embeddings for scientific text"
    - "combining mixmatch and active learning for better accuracy with fewer labels"

Connected through hyperedge HE-2 (induced by source paper "teddy a system for interactive review analysis"):
  with members:
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "foresight rapid data exploration through guideposts"
    - "thumbs up sentiment classification using machine learning techniques"

Connected through hyperedge HE-3 (induced by source paper "jo the smart journal"):
  with members:
    - "voyageur an experiential travel search engine"
    - "happydb a corpus of 100 000 crowdsourced happy moments"

Connected through hyperedge HE-4 (induced by source paper "voyageur an experiential travel search engine"):
  with members:
    - "open domain suggestion mining problem definition and datasets"

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.DB(Databases)