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: "matchzoo a toolkit for deep text matching"
  Abstract: In recent years, deep neural models have been widely adopted for text matching tasks, such as question answering and information retrieval, showing improved performance as compared with previous methods. In this paper, we introduce the MatchZoo toolkit that aims to facilitate the designing, comparing and sharing of deep text matching models. Specifically, the toolkit provides a unified data preparation module for different text matching problems, a flexible layer-based model construction process, and a variety of training objectives and evaluation metrics. In addition, the toolkit has implemented two schools of representative deep text matching models, namely representation-focused models and interaction-focused models. Finally, users can easily modify existing models, create and share their own models for text matching in MatchZoo.

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 "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-3 (induced by source paper "lattice cnns for matching based chinese question answering"):
  with members:
    - "dating documents using graph convolution networks"
    - "chinese ner using lattice lstm"
    - "subword encoding in lattice lstm for chinese word segmentation"
    - "a graph to sequence model for amr to text generation"
    - "cross sentence n ary relation extraction with graph lstms"
    - "improved neural relation detection for knowledge base question answering"
    - "encoding sentences with graph convolutional networks for semantic role labeling"
    - "bilateral multi perspective matching for natural language sentences"

Connected through hyperedge HE-4 (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"
    - "neural models for information retrieval"

Connected through hyperedge HE-5 (induced by source paper "impact of training dataset size on neural answer selection models"):
  with members:
    - "anmm ranking short answer texts with attention based neural matching model"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "neural models for information retrieval"
    - "deep learning scaling is predictable empirically"
    - "a deep relevance matching model for ad hoc retrieval"
    - "understanding deep learning requires rethinking generalization"
    - "learning to match using local and distributed representations of text for web search"
    - "text matching as image recognition"

Connected through hyperedge HE-6 (induced by source paper "a study on the interpretability of neural retrieval models using deepshap"):
  with members:
    - "exs explainable search using local model agnostic interpretability"
    - "deep relevance ranking using enhanced document query interactions"
    - "interpreting search result rankings through intent modeling"
    - "a unified approach to interpreting model predictions"
    - "learning important features through propagating activation differences"
    - "what is relevant in a text document an interpretable machine learning approach"
    - "a deep relevance matching model for ad hoc retrieval"
    - "why should i trust you explaining the predictions of any classifier"

Connected through hyperedge HE-7 (induced by source paper "matchzoo a learning practicing and developing system for neural text matching"):
  with members:
    - "tf ranking scalable tensorflow library for learning to rank"
    - "end to end neural ad hoc ranking with kernel pooling"
    - "a deep relevance matching model for ad hoc retrieval"
    - "text matching as image recognition"
    - "convolutional neural network architectures for matching natural language sentences"
    - "a deep architecture for semantic matching with multiple positional sentence representations"

Connected through hyperedge HE-8 (induced by source paper "matching article pairs with graphical decomposition and convolutions"):
  with members:
    - "multiresolution graph attention networks for relevance matching"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "matching natural language sentences with hierarchical sentence factorization"
    - "growing story forest online from massive breaking news"
    - "representation learning on graphs methods and applications"
    - "learning to match using local and distributed representations of text for web search"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "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)