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: "knowledge enhanced hybrid neural network for text matching"
  Abstract: Long text brings a big challenge to semantic matching due to their complicated semantic and syntactic structures. To tackle the challenge, we consider using prior knowledge to help identify useful information and filter out noise to matching in long text. To this end, we propose a knowledge enhanced hybrid neural network (KEHNN). The model fuses prior knowledge into word representations by knowledge gates and establishes three matching channels with words, sequential structures of sentences given by Gated Recurrent Units (GRU), and knowledge enhanced representations. The three channels are processed by a convolutional neural network to generate high level features for matching, and the features are synthesized as a matching score by a multilayer perceptron. The model extends the existing methods by conducting matching on words, local structures of sentences, and global context of sentences. Evaluation results from extensive experiments on public data sets for question answering and conversation show that KEHNN can significantly outperform the-state-of-the-art matching models and particularly improve the performance on pairs with long text.

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 "kinn incorporating expert knowledge in neural networks"):
  with members:
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "sequence to sequence models can directly translate foreign speech"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "state of the art speech recognition with sequence to sequence models"
    - "a semantic loss function for deep learning with symbolic knowledge"
    - "harnessing deep neural networks with logic rules"
    - "deep residual learning for image recognition"
    - "intriguing properties of neural networks"

Connected through hyperedge HE-3 (induced by source paper "co stack residual affinity networks with multi level attention refinement for matching text sequences"):
  with members:
    - "multi perspective relevance matching with hierarchical convnets for social media search"
    - "deep contextualized word representations"
    - "dr bilstm dependent reading bidirectional lstm for natural language inference"
    - "a continuously growing dataset of sentential paraphrases"
    - "natural language inference over interaction space"
    - "learned in translation contextualized word vectors"
    - "shortcut stacked sentence encoders for multi domain inference"
    - "compare compress and propagate enhancing neural architectures with alignment factorization for natural language inference"

Connected through hyperedge HE-4 (induced by source paper "multi cast attention networks for retrieval based question answering and response prediction"):
  with members:
    - "anmm ranking short answer texts with attention based neural matching model"
    - "hyperbolic representation learning for fast and efficient neural question answering"
    - "multi pointer co attention networks for recommendation"
    - "end to end neural ad hoc ranking with kernel pooling"
    - "learning to rank question answer pairs with holographic dual lstm architecture"
    - "neural ranking models with weak supervision"
    - "compare compress and propagate enhancing neural architectures with alignment factorization for natural language inference"
    - "word entity duet representations for document ranking"

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.CL(Computation and Language)