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 vector spaces for unsupervised information retrieval"
  Abstract: We propose the Neural Vector Space Model (NVSM), a method that learns representations of documents in an unsupervised manner for news article retrieval. In the NVSM paradigm, we learn low-dimensional representations of words and documents from scratch using gradient descent and rank documents according to their similarity with query representations that are composed from word representations. We show that NVSM performs better at document ranking than existing latent semantic vector space methods. The addition of NVSM to a mixture of lexical language models and a state-of-the-art baseline vector space model yields a statistically significant increase in retrieval effectiveness. Consequently, NVSM adds a complementary relevance signal. Next to semantic matching, we find that NVSM performs well in cases where lexical matching is needed. NVSM learns a notion of term specificity directly from the document collection without feature engineering. We also show that NVSM learns regularities related to Luhn significance. Finally, we give advice on how to deploy NVSM in situations where model selection (e.g., cross-validation) is infeasible. We find that an unsupervised ensemble of multiple models trained with different hyperparameter values performs better than a single cross-validated model. Therefore, NVSM can safely be used for ranking documents without supervised relevance judgments.

Connected through hyperedge HE-1 (induced by source paper "a deep look into neural ranking models for information retrieval"):
  with members:
    - "learning groupwise scoring functions using deep neural networks"
    - "simple applications of bert for ad hoc document retrieval"
    - "semeval 2017 task 3 community question answering"
    - "user intent prediction in information seeking conversations"
    - "vitor learning to rank webpages based on visual features"
    - "wikipassageqa a benchmark collection for research on non factoid answer passage retrieval"
    - "anmm ranking short answer texts with attention based neural matching model"
    - "cross domain regularization for neural ranking models using adversarial learning"

Connected through hyperedge HE-2 (induced by source paper "dataset search a survey"):
  with members:
    - "interactive summarization and exploration of top aggregate query answers"
    - "ad hoc table retrieval using semantic similarity"
    - "cuttlefish a lightweight primitive for adaptive query processing"
    - "on the fly table generation"
    - "everything you always wanted to know about a dataset studies in data summarisation"
    - "a survey on data collection for machine learning a big data ai integration perspective"
    - "entitables smart assistance for entity focused tables"
    - "navigating the data lake with datamaran automatically extracting structure from log datasets"

Connected through hyperedge HE-3 (induced by source paper "critically examining the neural hype weak baselines and the additivity of effectiveness gains from neural ranking models"):
  with members:
    - "simple applications of bert for ad hoc document retrieval"
    - "cedr contextualized embeddings for document ranking"
    - "end to end open domain question answering with"
    - "anmm ranking short answer texts with attention based neural matching model"
    - "multi perspective relevance matching with hierarchical convnets for social media search"
    - "end to end neural ad hoc ranking with kernel pooling"
    - "matchzoo a toolkit for deep text matching"
    - "neural models for information retrieval"

Connected through hyperedge HE-4 (induced by source paper "neural networks for information retrieval"):
  with members:
    - "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"
    - "deep reinforcement learning for dialogue generation"

Connected through hyperedge HE-5 (induced by source paper "unsupervised low dimensional vector representations for words phrases and text that are transparent scalable and produce similarity metrics that are complementary to neural embeddings"):
  with members:
    - "spine sparse interpretable neural embeddings"
    - "using distributed representations to disambiguate biomedical and clinical concepts"
    - "distributed representations of sentences and documents"

Connected through hyperedge HE-6 (induced by source paper "ilps at trec 2017 common core track"):
  with members:
    - "pyndri a python interface to the indri search engine"
    - "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:
    - "semantic entity retrieval toolkit"
    - "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"

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)