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: "understanding the logical and semantic structure of large documents"
  Abstract: Current language understanding approaches focus on small documents, such as newswire articles, blog posts, product reviews and discussion forum entries. Understanding and extracting information from large documents like legal briefs, proposals, technical manuals and research articles is still a challenging task. We describe a framework that can analyze a large document and help people to know where a particular information is in that document. We aim to automatically identify and classify semantic sections of documents and assign consistent and human-understandable labels to similar sections across documents. A key contribution of our research is modeling the logical and semantic structure of an electronic document. We apply machine learning techniques, including deep learning, in our prototype system. We also make available a dataset of information about a collection of scholarly articles from the arXiv eprints collection that includes a wide range of metadata for each article, including a table of contents, section labels, section summarizations and more. We hope that this dataset will be a useful resource for the machine learning and NLP communities in information retrieval, content-based question answering and language modeling.

Connected through hyperedge HE-1 (induced by source paper "unfolding the structure of a document using deep learning"):
  with members:
    - "understanding and representing the semantics of large structured documents"
    - "contextual lstm clstm models for large scale nlp tasks"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "text understanding from scratch"
    - "generating news headlines with recurrent neural networks"
    - "convolutional neural networks for sentence classification"
    - "sequence to sequence learning with neural networks"
    - "modeling documents with deep boltzmann machines"

Connected through hyperedge HE-2 (induced by source paper "table of contents generation on contemporary documents"):
  with members:
    - "a supervised learning approach for heading detection"
    - "xgboost a scalable tree boosting system"
    - "character level convolutional networks for text classification"
    - "adam a method for stochastic optimization"
    - "convolutional neural networks for sentence classification"
    - "improving neural networks by preventing co adaptation of feature detectors"

Connected through hyperedge HE-3 (induced by source paper "understanding and representing the semantics of large structured documents"):
  with members:
    - "a deep convolutional auto encoder with pooling unpooling layers in caffe"
    - "contextual lstm clstm models for large scale nlp tasks"
    - "generating news headlines with recurrent neural networks"
    - "modeling documents with deep boltzmann machines"

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)