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: "neural information retrieval a literature review"
  Abstract: A recent "third wave" of Neural Network (NN) approaches now delivers state-of-the-art performance in many machine learning tasks, spanning speech recognition, computer vision, and natural language processing. Because these modern NNs often comprise multiple interconnected layers, this new NN research is often referred to as deep learning. Stemming from this tide of NN work, a number of researchers have recently begun to investigate NN approaches to Information Retrieval (IR). While deep NNs have yet to achieve the same level of success in IR as seen in other areas, the recent surge of interest and work in NNs for IR suggest that this state of affairs may be quickly changing. In this work, we survey the current landscape of Neural IR research, paying special attention to the use of learned representations of queries and documents (i.e., neural embeddings). We highlight the successes of neural IR thus far, catalog obstacles to its wider adoption, and suggest potentially promising directions for future research.

Connected through hyperedge HE-1 (induced by source paper "an analysis of word2vec for the italian language"):
  with members:
    - "a survey of word embeddings evaluation methods"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-2 (induced by source paper "learning to reformulate the queries on the web"):
  with members:
    - "neural machine transliteration preliminary results"
    - "deep sentence embedding using long short term memory networks analysis and application to information retrieval"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "a character level decoder without explicit segmentation for neural machine translation"
    - "character aware neural language models"
    - "a neural conversational model"
    - "adam a method for stochastic optimization"
    - "on the properties of neural machine translation encoder decoder approaches"

Connected through hyperedge HE-3 (induced by source paper "interactive attention for semantic text matching"):
  with members:
    - "a fast deep learning model for textual relevance in biomedical information retrieval"
    - "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"
    - "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-4 (induced by source paper "data curation with deep learning vision towards self driving data curation"):
  with members:
    - "fine tuned language models for text classification"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "deep learning a critical appraisal"
    - "a survey of methods for explaining black box models"
    - "deeper deep entity resolution"
    - "mida multiple imputation using denoising autoencoders"
    - "unsupervised string transformation learning for entity consolidation"
    - "snorkel rapid training data creation with weak supervision"

Connected through hyperedge HE-5 (induced by source paper "data curation with deep learning vision"):
  with members:
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "universal language model fine tuning for text classification"
    - "deeper deep entity resolution"
    - "snorkel rapid training data creation with weak supervision"
    - "holoclean holistic data repairs with probabilistic inference"
    - "a survey on network embedding"
    - "graph2vec learning distributed representations of graphs"

Connected through hyperedge HE-6 (induced by source paper "a fast deep learning model for textual relevance in biomedical information retrieval"):
  with members:
    - "end to end neural ad hoc ranking with kernel pooling"
    - "pacrr a position aware neural ir model for relevance matching"
    - "neural models for information retrieval"
    - "a deep relevance matching model for ad hoc retrieval"
    - "query expansion with locally trained word embeddings"
    - "learning to match using local and distributed representations of text for web search"
    - "text matching as image recognition"
    - "convolutional neural network architectures for matching natural language sentences"

Connected through hyperedge HE-7 (induced by source paper "expeditious generation of knowledge graph embeddings"):
  with members:
    - "modeling relation paths for representation learning of knowledge bases"
    - "locally adaptive translation for knowledge graph embedding"
    - "holographic embeddings of knowledge graphs"
    - "transg a generative mixture model for knowledge graph embedding"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-8 (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"
    - "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"
    - "unsupervised efficient and semantic expertise retrieval"
    - "learning latent vector spaces for product 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)