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 models for information retrieval"
  Abstract: Neural ranking models for information retrieval (IR) use shallow or deep neural networks to rank search results in response to a query. Traditional learning to rank models employ machine learning techniques over hand-crafted IR features. By contrast, neural models learn representations of language from raw text that can bridge the gap between query and document vocabulary. Unlike classical IR models, these new machine learning based approaches are data-hungry, requiring large scale training data before they can be deployed. This tutorial introduces basic concepts and intuitions behind neural IR models, and places them in the context of traditional retrieval models. We begin by introducing fundamental concepts of IR and different neural and non-neural approaches to learning vector representations of text. We then review shallow neural IR methods that employ pre-trained neural term embeddings without learning the IR task end-to-end. We introduce deep neural networks next, discussing popular deep architectures. Finally, we review the current DNN models for information retrieval. We conclude with a discussion on potential future directions for neural IR.

Connected through hyperedge HE-1 (induced by source paper "separate and attend in personal email search"):
  with members:
    - "spherical text embedding"
    - "tf ranking scalable tensorflow library for learning to rank"
    - "area attention"
    - "domain adaptation for enterprise email search"
    - "multi task learning for email search ranking with auxiliary query clustering"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "deeprank a new deep architecture for relevance ranking in information retrieval"
    - "attention is all you need"

Connected through hyperedge HE-2 (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-3 (induced by source paper "representation learning for words and entities"):
  with members:
    - "deep multi view learning to rank"
    - "setexpan corpus based set expansion via context feature selection and rank ensemble"
    - "deep contextualized word representations"
    - "deep sets"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "representation learning of entities and documents from knowledge base descriptions"
    - "new embedded representations and evaluation protocols for inferring transitive relations"
    - "neural collaborative filtering"

Connected through hyperedge HE-4 (induced by source paper "neural ir meets graph embedding a ranking model for product search"):
  with members:
    - "graph convolutional neural networks for web scale recommender systems"
    - "entity duet neural ranking understanding the role of knowledge graph semantics in neural information retrieval"
    - "graph embedding techniques applications and performance a survey"
    - "deeprank a new deep architecture for relevance ranking in information retrieval"
    - "semantic entity retrieval toolkit"
    - "ensemble methods for personalized e commerce search challenge at cikm cup 2016"
    - "mimicking word embeddings using subword rnns"
    - "geometric matrix completion with recurrent multi graph neural networks"

Connected through hyperedge HE-5 (induced by source paper "domain adaptation for enterprise email search"):
  with members:
    - "tf ranking scalable tensorflow library for learning to rank"
    - "multi task learning for email search ranking with auxiliary query clustering"
    - "cross domain regularization for neural ranking models using adversarial learning"
    - "neural ranking models with weak supervision"
    - "adversarial discriminative domain adaptation"
    - "stabilizing training of generative adversarial networks through regularization"
    - "improved training of wasserstein gans"
    - "deep reconstruction classification networks for unsupervised domain adaptation"

Connected through hyperedge HE-6 (induced by source paper "learning fast matching models from weak annotations"):
  with members:
    - "learning matching models with weak supervision for response selection in retrieval based chatbots"
    - "anmm ranking short answer texts with attention based neural matching model"
    - "a deep relevance matching model for ad hoc retrieval"
    - "a decomposable attention model for natural language inference"
    - "learning to match using local and distributed representations of text for web search"
    - "deep model compression distilling knowledge from noisy teachers"
    - "abcnn attention based convolutional neural network for modeling sentence pairs"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-7 (induced by source paper "tf ranking scalable tensorflow library for learning to rank"):
  with members:
    - "learning groupwise scoring functions using deep neural networks"
    - "end to end neural ad hoc ranking with kernel pooling"
    - "tensorflow serving flexible high performance ml serving"
    - "tensorflow estimators managing simplicity vs flexibility in high level machine learning frameworks"
    - "unbiased learning to rank with biased feedback"
    - "tensorflow a system for large scale machine learning"
    - "mxnet a flexible and efficient machine learning library for heterogeneous distributed systems"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-8 (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"
    - "neural vector spaces for unsupervised information retrieval"
    - "end to end neural ad hoc ranking with kernel pooling"
    - "matchzoo a toolkit for deep text matching"

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)