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: "convolutional neural network architectures for matching natural language sentences"
  Abstract: Semantic matching is of central importance to many natural language tasks \cite{bordes2014semantic,RetrievalQA}. A successful matching algorithm needs to adequately model the internal structures of language objects and the interaction between them. As a step toward this goal, we propose convolutional neural network models for matching two sentences, by adapting the convolutional strategy in vision and speech. The proposed models not only nicely represent the hierarchical structures of sentences with their layer-by-layer composition and pooling, but also capture the rich matching patterns at different levels. Our models are rather generic, requiring no prior knowledge on language, and can hence be applied to matching tasks of different nature and in different languages. The empirical study on a variety of matching tasks demonstrates the efficacy of the proposed model on a variety of matching tasks and its superiority to competitor models.

Connected through hyperedge HE-1 (induced by source paper "selective weak supervision for neural information retrieval"):
  with members:
    - "latent retrieval for weakly supervised open domain question answering"
    - "passage re ranking with bert"
    - "generic intent representation in web search"
    - "document expansion by query prediction"
    - "critically examining the neural hype weak baselines and the additivity of effectiveness gains from neural ranking models"
    - "deeper text understanding for ir with contextual neural language modeling"
    - "understanding the behaviors of bert in ranking"
    - "cedr contextualized embeddings for document ranking"

Connected through hyperedge HE-2 (induced by source paper "iart intent aware response ranking with transformers in information seeking conversation systems"):
  with members:
    - "a hybrid retrieval generation neural conversation model"
    - "attentive history selection for conversational question answering"
    - "user intent prediction in information seeking conversations"
    - "a deep look into neural ranking models for information retrieval"
    - "anmm ranking short answer texts with attention based neural matching model"
    - "analyzing and characterizing user intent in information seeking conversations"
    - "neural approaches to conversational ai"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-3 (induced by source paper "learning contextualized document representations for healthcare answer retrieval"):
  with members:
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "learning dense representations for entity retrieval"
    - "a question entailment approach to question answering"
    - "pubmedqa a dataset for biomedical research question answering"
    - "zero shot entity linking by reading entity descriptions"
    - "sector a neural model for coherent topic segmentation and classification"
    - "matchzoo a learning practicing and developing system for neural text matching"
    - "anmm ranking short answer texts with attention based neural matching model"

Connected through hyperedge HE-4 (induced by source paper "text based question answering from information retrieval and deep neural network perspectives a survey"):
  with members:
    - "a compare aggregate model with latent clustering for answer selection"
    - "bertsel answer selection with pre trained models"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "sanity check a strong alignment and information retrieval baseline for question answering"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "learning to rank question answer pairs with holographic dual lstm architecture"
    - "bilateral multi perspective matching for natural language sentences"

Connected through hyperedge HE-5 (induced by source paper "neural relation prediction for simple question answering over knowledge graph"):
  with members:
    - "anmm ranking short answer texts with attention based neural matching model"
    - "improved neural relation detection for knowledge base question answering"
    - "end to end neural ad hoc ranking with kernel pooling"
    - "strong baselines for simple question answering over knowledge graphs with and without neural networks"
    - "bilateral multi perspective matching for natural language sentences"
    - "attentive pooling networks"
    - "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-6 (induced by source paper "simgnn a neural network approach to fast graph similarity computation"):
  with members:
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "drug similarity integration through attentive multi view graph auto encoders"
    - "hierarchical graph representation learning with differentiable pooling"
    - "modeling polypharmacy side effects with graph convolutional networks"
    - "dynamic edge conditioned filters in convolutional neural networks on graphs"
    - "representation learning on graphs methods and applications"
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"

Connected through hyperedge HE-7 (induced by source paper "dropout prediction over weeks in moocs via interpretable multi layer representation learning"):
  with members:
    - "skip thought vectors"
    - "likely to stop predicting stopout in massive open online courses"
    - "your click decides your fate leveraging clickstream patterns in mooc videos to infer students information processing and attrition behavior"
    - "capturing attrition intensifying structural traits from didactic interaction sequences of mooc learners"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"
    - "representation learning a review and new perspectives"

Connected through hyperedge HE-8 (induced by source paper "differentially private set union"):
  with members:
    - "diversifying reply suggestions using a matching conditional variational autoencoder"
    - "differentially private hierarchical group size estimation"
    - "collecting telemetry data privately"
    - "smart reply automated response suggestion for email"
    - "calibrating data to sensitivity in private data analysis"

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)