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 study of matchpyramid models on ad hoc retrieval"
  Abstract: Deep neural networks have been successfully applied to many text matching tasks, such as paraphrase identification, question answering, and machine translation. Although ad-hoc retrieval can also be formalized as a text matching task, few deep models have been tested on it. In this paper, we study a state-of-the-art deep matching model, namely MatchPyramid, on the ad-hoc retrieval task. The MatchPyramid model employs a convolutional neural network over the interactions between query and document to produce the matching score. We conducted extensive experiments to study the impact of different pooling sizes, interaction functions and kernel sizes on the retrieval performance. Finally, we show that the MatchPyramid models can significantly outperform several recently introduced deep matching models on the retrieval task, but still cannot compete with the traditional retrieval models, such as BM25 and language models.

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 "overcoming low utility facets for complex answer retrieval"):
  with members:
    - "characterizing question facets for complex answer retrieval"
    - "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"
    - "co pacrr a context aware neural ir model for ad hoc retrieval"
    - "word entity duet representations for document ranking"
    - "benchmark for complex answer retrieval"
    - "pacrr a position aware neural ir model for relevance matching"

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"
    - "learning to match using local and distributed representations of text for web search"

Connected through hyperedge HE-4 (induced by source paper "learning groupwise scoring functions using deep neural networks"):
  with members:
    - "tf ranking scalable tensorflow library for learning to rank"
    - "learning a deep listwise context model for ranking refinement"
    - "seq2slate re ranking and slate optimization with rnns"
    - "deeprank a new deep architecture for relevance ranking in information retrieval"
    - "neural ranking models with weak supervision"
    - "unbiased learning to rank with biased feedback"
    - "a deep relevance matching model for ad hoc retrieval"
    - "learning to match using local and distributed representations of text for web search"

Connected through hyperedge HE-5 (induced by source paper "attentive long short term preference modeling for personalized product search"):
  with members:
    - "review driven answer generation for product related questions in e commerce"
    - "mmalfm explainable recommendation by leveraging reviews and images"
    - "aspect aware latent factor model rating prediction with ratings and reviews"
    - "deeprank a new deep architecture for relevance ranking in information retrieval"
    - "ensemble methods for personalized e commerce search challenge at cikm cup 2016"
    - "neural collaborative filtering"
    - "neural attentive session based recommendation"
    - "attentional factorization machines learning the weight of feature interactions via attention networks"

Connected through hyperedge HE-6 (induced by source paper "deeptilebars visualizing term distribution for neural information retrieval"):
  with members:
    - "modeling diverse relevance patterns in ad hoc retrieval"
    - "attention based neural text segmentation"
    - "deeprank a new deep architecture for relevance ranking in information retrieval"
    - "end to end neural ad hoc ranking with kernel pooling"
    - "learning visual features from snapshots for web search"
    - "pacrr a position aware neural ir model for relevance matching"
    - "dsrim a deep neural information retrieval model enhanced by a knowledge resource driven representation of documents"
    - "a deep relevance matching model for ad hoc retrieval"

Connected through hyperedge HE-7 (induced by source paper "modeling diverse relevance patterns in ad hoc retrieval"):
  with members:
    - "deeprank a new deep architecture for relevance ranking in information retrieval"
    - "neural models for information retrieval"
    - "a deep relevance matching model for ad hoc retrieval"
    - "learning to match using local and distributed representations of text for web search"
    - "match srnn modeling the recursive matching structure with spatial rnn"
    - "convolutional neural network architectures for matching natural language sentences"
    - "two stage document length normalization for information retrieval"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "nprf a neural pseudo relevance feedback framework for ad hoc information retrieval"):
  with members:
    - "modeling diverse relevance patterns in ad hoc retrieval"
    - "deeprank a new deep architecture for relevance ranking in information retrieval"
    - "end to end neural ad hoc ranking with kernel pooling"
    - "co pacrr a context aware neural ir model for ad hoc 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"
    - "adam a method for stochastic optimization"

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)