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 dual embedding space model for document ranking"
  Abstract: A fundamental goal of search engines is to identify, given a query, documents that have relevant text. This is intrinsically difficult because the query and the document may use different vocabulary, or the document may contain query words without being relevant. We investigate neural word embeddings as a source of evidence in document ranking. We train a word2vec embedding model on a large unlabelled query corpus, but in contrast to how the model is commonly used, we retain both the input and the output projections, allowing us to leverage both the embedding spaces to derive richer distributional relationships. During ranking we map the query words into the input space and the document words into the output space, and compute a query-document relevance score by aggregating the cosine similarities across all the query-document word pairs. #R##N#We postulate that the proposed Dual Embedding Space Model (DESM) captures evidence on whether a document is about a query term in addition to what is modelled by traditional term-frequency based approaches. Our experiments show that the DESM can re-rank top documents returned by a commercial Web search engine, like Bing, better than a term-matching based signal like TF-IDF. However, when ranking a larger set of candidate documents, we find the embeddings-based approach is prone to false positives, retrieving documents that are only loosely related to the query. We demonstrate that this problem can be solved effectively by ranking based on a linear mixture of the DESM and the word counting features.

Connected through hyperedge HE-1 (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"
    - "query expansion with locally trained word embeddings"
    - "learning to match using local and distributed representations of text for web search"
    - "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-2 (induced by source paper "duet at trec 2019 deep learning track"):
  with members:
    - "overview of the trec 2019 deep learning track"
    - "passage re ranking with bert"
    - "incorporating query term independence assumption for efficient retrieval and ranking using deep neural networks"
    - "an updated duet model for passage re ranking"
    - "cross domain regularization for neural ranking models using adversarial learning"
    - "neural ranking models with multiple document fields"
    - "benchmark for complex answer retrieval"
    - "learning to match using local and distributed representations of text for web search"

Connected through hyperedge HE-3 (induced by source paper "improving low resource cross lingual document retrieval by reranking with deep bilingual representations"):
  with members:
    - "deep relevance ranking using enhanced document query interactions"
    - "end to end neural ad hoc ranking with kernel pooling"
    - "co pacrr a context aware neural ir model for ad hoc retrieval"
    - "pacrr a position aware neural ir model for relevance matching"
    - "deep architectures for neural machine translation"
    - "word translation without parallel data"
    - "a deep relevance matching model for ad hoc retrieval"
    - "a study of matchpyramid models on ad hoc retrieval"

Connected through hyperedge HE-4 (induced by source paper "affect enriched word embeddings for news information retrieval"):
  with members:
    - "toward incorporation of relevant documents in word2vec"
    - "relevance based word embedding"
    - "using word embeddings for automatic query expansion"
    - "representing documents and queries as sets of word embedded vectors for information retrieval"
    - "integrating distributional lexical contrast into word embeddings for antonym synonym distinction"
    - "retrofitting word vectors to semantic lexicons"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-5 (induced by source paper "relemb a relevance based application embedding for mobile app retrieval and categorization"):
  with members:
    - "target apps selection towards a unified search framework for mobile devices"
    - "mrnet product2vec a multi task recurrent neural network for product embeddings"
    - "relevance based word embedding"
    - "query expansion with locally trained word embeddings"
    - "distributed representations of sentences and documents"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-6 (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-7 (induced by source paper "ad hoc table retrieval using semantic similarity"):
  with members:
    - "entitables smart assistance for entity focused tables"
    - "design patterns for fusion based object retrieval"
    - "word entity duet representations for document ranking"
    - "scientific table search using keyword queries"
    - "modelling user preferences using word embeddings for context aware venue recommendation"
    - "neural enquirer learning to query tables with natural language"
    - "line large scale information network embedding"
    - "deepwalk online learning of social representations"

Connected through hyperedge HE-8 (induced by source paper "exs explainable search using local model agnostic interpretability"):
  with members:
    - "posthoc interpretability of learning to rank models using secondary training data"
    - "a causal framework for explaining the predictions of black box sequence to sequence models"
    - "irgan a minimax game for unifying generative and discriminative information retrieval models"
    - "neural models for information retrieval"
    - "variational deep semantic hashing for text documents"
    - "a deep relevance matching model for ad hoc retrieval"
    - "query expansion with locally trained word embeddings"
    - "why should i trust you explaining the predictions of any classifier"

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)