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: "fonduer knowledge base construction from richly formatted data"
  Abstract: We focus on knowledge base construction (KBC) from richly formatted data. In contrast to KBC from text or tabular data, KBC from richly formatted data aims to extract relations conveyed jointly via textual, structural, tabular, and visual expressions. We introduce Fonduer, a machine-learning-based KBC system for richly formatted data. Fonduer presents a new data model that accounts for three challenging characteristics of richly formatted data: (1) prevalent document-level relations, (2) multimodality, and (3) data variety. Fonduer uses a new deep-learning model to automatically capture the representation (i.e., features) needed to learn how to extract relations from richly formatted data. Finally, Fonduer provides a new programming model that enables users to convert domain expertise, based on multiple modalities of information, to meaningful signals of supervision for training a KBC system. Fonduer-based KBC systems are in production for a range of use cases, including at a major online retailer. We compare Fonduer against state-of-the-art KBC approaches in four different domains. We show that Fonduer achieves an average improvement of 41 F1 points on the quality of the output knowledge base---and in some cases produces up to 1.87x the number of correct entries---compared to expert-curated public knowledge bases. We also conduct a user study to assess the usability of Fonduer's new programming model. We show that after using Fonduer for only 30 minutes, non-domain experts are able to design KBC systems that achieve on average 23 F1 points higher quality than traditional machine-learning-based KBC approaches.

Connected through hyperedge HE-1 (induced by source paper "doc2edag an end to end document level framework for chinese financial event extraction"):
  with members:
    - "one for all neural joint modeling of entities and events"
    - "event extraction with generative adversarial imitation learning"
    - "reinforcement learning for relation classification from noisy data"
    - "diag nre a neural pattern diagnosis framework for distantly supervised neural relation extraction"
    - "attention is all you need"
    - "joint extraction of entities and relations based on a novel tagging scheme"
    - "joint extraction of events and entities within a document context"
    - "cotype joint extraction of typed entities and relations with knowledge bases"

Connected through hyperedge HE-2 (induced by source paper "document level n ary relation extraction with multiscale representation learning"):
  with members:
    - "simultaneously self attending to all mentions for full abstract biological relation extraction"
    - "n ary relation extraction using graph state lstm"
    - "deep probabilistic logic a unifying framework for indirect supervision"
    - "cross sentence n ary relation extraction with graph lstms"
    - "attention is all you need"
    - "distant supervision for relation extraction beyond the sentence boundary"
    - "joint extraction of events and entities within a document context"
    - "hierarchical multiscale recurrent neural networks"

Connected through hyperedge HE-3 (induced by source paper "building chatbots from large scale domain specific knowledge bases challenges and opportunities"):
  with members:
    - "beyond word embeddings learning entity and concept representations from large scale knowledge bases"
    - "just ask building an architecture for extensible self service spoken language understanding"
    - "effective approaches to attention based neural machine translation"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-4 (induced by source paper "snorkel rapid training data creation with weak supervision"):
  with members:
    - "snorkel drybell a case study in deploying weak supervision at industrial scale"
    - "training classifiers with natural language explanations"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "inferring generative model structure with static analysis"
    - "holoclean holistic data repairs with probabilistic inference"
    - "learning the structure of generative models without labeled data"
    - "label free supervision of neural networks with physics and domain knowledge"
    - "tensorflow a system for large scale machine learning"

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)