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: "a fast deep learning model for textual relevance in biomedical information retrieval"
  Abstract: Publications in the life sciences are characterized by a large technical vocabulary, with many lexical and semantic variations for expressing the same concept. Towards addressing the problem of relevance in biomedical literature search, we introduce a deep learning model for the relevance of a document's text to a keyword style query. Limited by a relatively small amount of training data, the model uses pre-trained word embeddings. With these, the model first computes a variable-length Delta matrix between the query and document, representing a difference between the two texts, which is then passed through a deep convolution stage followed by a deep feed-forward network to compute a relevance score. This results in a fast model suitable for use in an online search engine. The model is robust and outperforms comparable state-of-the-art deep learning approaches.

Connected through hyperedge HE-1 (induced by source paper "graphene a precise biomedical literature retrieval engine with graph augmented deep learning and external knowledge empowerment"):
  with members:
    - "scibert a pretrained language model for scientific text"
    - "scibert pretrained contextualized embeddings for scientific text"
    - "n ary relation extraction using graph state lstm"
    - "graphie a graph based framework for information extraction"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a neural multi task learning framework to jointly model medical named entity recognition and normalization"
    - "end to end neural ad hoc ranking with kernel pooling"
    - "pacrr a position aware neural ir model for relevance matching"

Connected through hyperedge HE-2 (induced by source paper "interactive attention for semantic text matching"):
  with members:
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "pacrr a position aware neural ir model for relevance matching"
    - "a deep relevance matching model for ad hoc retrieval"
    - "neural information retrieval a literature review"
    - "convolutional neural network architectures for matching natural language sentences"
    - "a deep architecture for semantic matching with multiple positional sentence representations"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-3 (induced by source paper "personalized neural language models for real world query auto completion"):
  with members:
    - "diverse beam search decoding diverse solutions from neural sequence models"
    - "character aware neural language models"
    - "on the properties of neural machine translation encoder decoder approaches"
    - "distributed representations of sentences and documents"
    - "distributed representations of words and phrases and their compositionality"

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.IR(Information Retrieval)