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: "query expansion with locally trained word embeddings"
  Abstract: Continuous space word embeddings have received a great deal of attention in the natural language processing and machine learning communities for their ability to model term similarity and other relationships. We study the use of term relatedness in the context of query expansion for ad hoc information retrieval. We demonstrate that word embeddings such as word2vec and GloVe, when trained globally, underperform corpus and query specific embeddings for retrieval tasks. These results suggest that other tasks benefiting from global embeddings may also benefit from local embeddings.

Connected through hyperedge HE-1 (induced by source paper "selective weak supervision for neural information retrieval"):
  with members:
    - "latent retrieval for weakly supervised open domain question answering"
    - "passage re ranking with bert"
    - "generic intent representation in web search"
    - "document expansion by query prediction"
    - "critically examining the neural hype weak baselines and the additivity of effectiveness gains from neural ranking models"
    - "deeper text understanding for ir with contextual neural language modeling"
    - "understanding the behaviors of bert in ranking"
    - "cedr contextualized embeddings for document ranking"

Connected through hyperedge HE-2 (induced by source paper "semantic speech retrieval with a visually grounded model of untranscribed speech"):
  with members:
    - "vision as an interlingua learning multilingual semantic embeddings of untranscribed speech"
    - "jointly discovering visual objects and spoken words from raw sensory input"
    - "linguistic unit discovery from multi modal inputs in unwritten languages summary of the speaking rosetta jsalt 2017 workshop"
    - "visually grounded learning of keyword prediction from untranscribed speech"
    - "visual features for context aware speech recognition"
    - "sequence to sequence models can directly translate foreign speech"
    - "learning word like units from joint audio visual analysis"
    - "learning modality invariant representations for speech and images"

Connected through hyperedge HE-3 (induced by source paper "semantic product search"):
  with members:
    - "anmm ranking short answer texts with attention based neural matching model"
    - "co pacrr a context aware neural ir model for ad hoc retrieval"
    - "pacrr a position aware neural ir model for relevance matching"
    - "a deep relevance matching model for ad hoc retrieval"
    - "learning to match using local and distributed representations of text for web search"
    - "a dual embedding space model for document ranking"
    - "bag of tricks for efficient text classification"
    - "deep sentence embedding using long short term memory networks analysis and application to information retrieval"

Connected through hyperedge HE-4 (induced by source paper "generic intent representation in web search"):
  with members:
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "deep contextualized word representations"
    - "universal sentence encoder"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "deeprank a new deep architecture for relevance ranking in information retrieval"
    - "end to end neural ad hoc ranking with kernel pooling"
    - "neural ranking models with multiple document fields"
    - "relevance based word embedding"

Connected through hyperedge HE-5 (induced by source paper "word embeddings for entity annotated texts"):
  with members:
    - "verse versatile graph embeddings from similarity measures"
    - "efficient graph computation for node2vec"
    - "graph embedding techniques applications and performance a survey"
    - "a survey of word embeddings evaluation methods"
    - "node2vec scalable feature learning for networks"
    - "enriching word vectors with subword information"
    - "joint learning of the embedding of words and entities for named entity disambiguation"
    - "line large scale information network embedding"

Connected through hyperedge HE-6 (induced by source paper "query expansion techniques for information retrieval a survey"):
  with members:
    - "just an update on pming distance for web based semantic similarity in artificial intelligence and data mining"
    - "using word embeddings for automatic query expansion"
    - "query expansion via structural motifs in wikipedia graph"
    - "query expansion based on crowd knowledge for code search"
    - "socializing the semantic gap a comparative survey on image tag assignment refinement and retrieval"
    - "exploring query categorisation for query expansion a study"
    - "query expansion using term distribution and term association"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-7 (induced by source paper "firsthand opiates abuse on social media monitoring geospatial patterns of interest through a digital cohort"):
  with members:
    - "the anatomy of reddit an overview of academic research"
    - "caveat emptor computational social science large scale missing data in a widely published reddit corpus"
    - "twitter for sparking a movement reddit for sharing the moment metoo through the lens of social media"
    - "a web of hate tackling hateful speech in online social spaces"
    - "the impact of crowds on news engagement a reddit case study"
    - "science askscience and badscience on the coexistence of highly related communities"
    - "all who wander on the prevalence and characteristics of multi community engagement"
    - "navigating the massive world of reddit using backbone networks to map user interests in social media"

Connected through hyperedge HE-8 (induced by source paper "on the effect of low frequency terms on neural ir models"):
  with members:
    - "allennlp a deep semantic natural language processing platform"
    - "deep contextualized word representations"
    - "consistency and variation in kernel neural ranking model"
    - "end to end neural ad hoc ranking with kernel pooling"
    - "enriching word vectors with subword information"
    - "ms marco a human generated machine reading comprehension dataset"
    - "text matching as image recognition"

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)