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: "semantic entity retrieval toolkit"
  Abstract: Unsupervised learning of low-dimensional, semantic representations of words and entities has recently gained attention. In this paper we describe the Semantic Entity Retrieval Toolkit (SERT) that provides implementations of our previously published entity representation models. The toolkit provides a unified interface to different representation learning algorithms, fine-grained parsing configuration and can be used transparently with GPUs. In addition, users can easily modify existing models or implement their own models in the framework. After model training, SERT can be used to rank entities according to a textual query and extract the learned entity/word representation for use in downstream algorithms, such as clustering or recommendation.

Connected through hyperedge HE-1 (induced by source paper "neural ir meets graph embedding a ranking model for product search"):
  with members:
    - "graph convolutional neural networks for web scale recommender systems"
    - "entity duet neural ranking understanding the role of knowledge graph semantics in neural information retrieval"
    - "graph embedding techniques applications and performance a survey"
    - "deeprank a new deep architecture for relevance ranking in information retrieval"
    - "ensemble methods for personalized e commerce search challenge at cikm cup 2016"
    - "mimicking word embeddings using subword rnns"
    - "geometric matrix completion with recurrent multi graph neural networks"
    - "representation learning on graphs methods and applications"

Connected through hyperedge HE-2 (induced by source paper "wiser a semantic approach for expert finding in academia based on entity linking"):
  with members:
    - "unsupervised efficient and semantic expertise retrieval"
    - "learning latent vector spaces for product search"
    - "learning to rank academic experts in the dblp dataset"
    - "deepwalk online learning of social representations"
    - "distributed representations of words and phrases and their compositionality"
    - "fast and accurate annotation of short texts with wikipedia pages"

Connected through hyperedge HE-3 (induced by source paper "neural vector spaces for unsupervised information retrieval"):
  with members:
    - "neural networks for information retrieval"
    - "efficient vector representation for documents through corruption"
    - "pyndri a python interface to the indri search engine"
    - "reply with proactive recommendation of email attachments"
    - "a deep relevance matching model for ad hoc retrieval"
    - "learning to match using local and distributed representations of text for web search"
    - "noisy activation functions"
    - "siamese cbow optimizing word embeddings for sentence representations"

Connected through hyperedge HE-4 (induced by source paper "remedies against the vocabulary gap in information retrieval"):
  with members:
    - "neural vector spaces for unsupervised information retrieval"
    - "pyndri a python interface to the indri search engine"
    - "reply with proactive recommendation of email attachments"
    - "unsupervised efficient and semantic expertise retrieval"
    - "recurrent memory network for language modeling"
    - "learning latent vector spaces for product search"
    - "adadelta an adaptive learning rate method"
    - "the numpy array a structure for efficient numerical computation"

Connected through hyperedge HE-5 (induced by source paper "structural regularities in text based entity vector spaces"):
  with members:
    - "efficient estimation of word representations in vector space"

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)