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: "unsupervised efficient and semantic expertise retrieval"
  Abstract: We introduce an unsupervised discriminative model for the task of retrieving experts in online document collections. We exclusively employ textual evidence and avoid explicit feature engineering by learning distributed word representations in an unsupervised way. We compare our model to state-of-the-art unsupervised statistical vector space and probabilistic generative approaches. Our proposed log-linear model achieves the retrieval performance levels of state-of-the-art document-centric methods with the low inference cost of so-called profile-centric approaches. It yields a statistically significant improved ranking over vector space and generative models in most cases, matching the performance of supervised methods on various benchmarks. That is, by using solely text we can do as well as methods that work with external evidence and/or relevance feedback. A contrastive analysis of rankings produced by discriminative and generative approaches shows that they have complementary strengths due to the ability of the unsupervised discriminative model to perform semantic matching.

Connected through hyperedge HE-1 (induced by source paper "quality aware skill translation models for expert finding on stackoverflow"):
  with members:
    - "learning latent vector spaces for product search"
    - "adadelta an adaptive learning rate method"

Connected through hyperedge HE-2 (induced by source paper "wiser a semantic approach for expert finding in academia based on entity linking"):
  with members:
    - "semantic entity retrieval toolkit"
    - "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 networks for information retrieval"):
  with members:
    - "neural vector spaces for unsupervised information retrieval"
    - "attentive memory networks efficient machine reading for conversational search"
    - "neural ranking models with weak supervision"
    - "reply with proactive recommendation of email attachments"
    - "a neural knowledge language model"
    - "learning to attend copy and generate for session based query suggestion"
    - "personalizing session based recommendations with hierarchical recurrent neural networks"
    - "avoiding your teacher s mistakes training neural networks with controlled weak supervision"

Connected through hyperedge HE-4 (induced by source paper "neural vector spaces for unsupervised information retrieval"):
  with members:
    - "neural networks for information retrieval"
    - "semantic entity retrieval toolkit"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "expert finding in heterogeneous bibliographic networks with locally trained embeddings"):
  with members:
    - "query expansion with locally trained word embeddings"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-6 (induced by source paper "neural ranking models with weak supervision"):
  with members:
    - "anmm ranking short answer texts with attention based neural matching model"
    - "relevance based word embedding"
    - "a deep relevance matching model for ad hoc retrieval"
    - "neural information retrieval a literature review"
    - "getting started with neural models for semantic matching in web search"
    - "query expansion with locally trained word embeddings"
    - "learning to match using local and distributed representations of text for web search"
    - "learning latent vector spaces for product search"

Connected through hyperedge HE-7 (induced by source paper "remedies against the vocabulary gap in information retrieval"):
  with members:
    - "neural vector spaces for unsupervised information retrieval"
    - "semantic entity retrieval toolkit"
    - "pyndri a python interface to the indri search engine"
    - "reply with proactive recommendation of email attachments"
    - "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-8 (induced by source paper "semantic entity retrieval toolkit"):
  with members:
    - "pyndri a python interface to the indri search engine"
    - "learning latent vector spaces for product search"
    - "off the beaten path let s replace term based retrieval with k nn search"

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)