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 deep network model for paraphrase detection in short text messages"
  Abstract: Abstract   This paper is concerned with paraphrase detection, i.e., identifying sentences that are semantically identical. The ability to detect similar sentences written in natural language is crucial for several applications, such as text mining, text summarization, plagiarism detection, authorship authentication and question answering. Recognizing this importance, we study in particular how to address the challenges with detecting paraphrases in user generated short texts, such as Twitter, which often contain language irregularity and noise, and do not necessarily contain as much semantic information as longer clean texts. We propose a novel deep neural network-based approach that relies on coarse-grained sentence modelling using a convolutional neural network (CNN) and a recurrent neural network (RNN) model, combined with a specific fine-grained word-level similarity matching model. More specifically, we develop a new architecture, called DeepParaphrase, which enables to create an informative semantic representation of each sentence by (1) using CNN to extract the local region information in form of important n-grams from the sentence, and (2) applying RNN to capture the long-term dependency information. In addition, we perform a comparative study on state-of-the-art approaches within paraphrase detection. An important insight from this study is that existing paraphrase approaches perform well when applied on clean texts, but they do not necessarily deliver good performance against noisy texts, and vice versa. In contrast, our evaluation has shown that the proposed DeepParaphrase-based approach achieves good results in both types of texts, thus making it more robust and generic than the existing approaches.

Connected through hyperedge HE-1 (induced by source paper "an enhanced tree lstm architecture for sentence semantic modeling using typed dependencies"):
  with members:
    - "recurrent neural network based semantic variational autoencoder for sequence to sequence learning"
    - "tag enhanced tree structured neural networks for implicit discourse relation classification"
    - "dynamic compositionality in recursive neural networks with structure aware tag representations"
    - "dynamic compositional neural networks over tree structure"
    - "learning to compose words into sentences with reinforcement learning"
    - "convolutional neural networks for sentence classification"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-2 (induced by source paper "deep contextualized pairwise semantic similarity for arabic language questions"):
  with members:
    - "no training required exploring random encoders for sentence classification"
    - "learning semantic textual similarity from conversations"
    - "deep contextualized word representations"
    - "coqa a conversational question answering challenge"
    - "universal sentence encoder"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "ordered neurons integrating tree structures into recurrent neural networks"
    - "unsupervised learning of sentence embeddings using compositional n gram features"

Connected through hyperedge HE-3 (induced by source paper "a multi cascaded model with data augmentation for enhanced paraphrase detection in short texts"):
  with members:
    - "a multi cascaded deep model for bilingual sms classification"
    - "deep contextualized word representations"
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "neural network models for paraphrase identification semantic textual similarity natural language inference and question answering"
    - "shortcut stacked sentence encoders for multi domain inference"
    - "the effectiveness of data augmentation in image classification using deep learning"
    - "reporting score distributions makes a difference performance study of lstm networks for sequence tagging"
    - "neural paraphrase identification of questions with noisy pretraining"

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)