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 compare aggregate model for matching text sequences"
  Abstract: Many NLP tasks including machine comprehension, answer selection and text entailment require the comparison between sequences. Matching the important units between sequences is a key to solve these problems. In this paper, we present a general "compare-aggregate" framework that performs word-level matching followed by aggregation using Convolutional Neural Networks. We particularly focus on the different comparison functions we can use to match two vectors. We use four different datasets to evaluate the model. We find that some simple comparison functions based on element-wise operations can work better than standard neural network and neural tensor network.

Connected through hyperedge HE-1 (induced by source paper "learning to respond with stickers a framework of unifying multi modality in multi turn dialog"):
  with members:
    - "image question answer synergistic network for visual dialog"
    - "understanding chat messages for sticker recommendation in hike messenger"
    - "learning a matching model with co teaching for multi turn response selection in retrieval based dialogue systems"
    - "product aware answer generation in e commerce question answering"
    - "how to write summaries with patterns learning towards abstractive summarization through prototype editing"
    - "abstractive text summarization by incorporating reader comments"
    - "think visually question answering through virtual imagery"
    - "multimodal emoji prediction"

Connected through hyperedge HE-2 (induced by source paper "a study on efficiency accuracy and document structure for answer sentence selection"):
  with members:
    - "passage re ranking with bert"
    - "tanda transfer and adapt pre trained transformer models for answer sentence selection"
    - "understanding the behaviors of bert in ranking"
    - "a gated self attention memory network for answer selection"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "energy and policy considerations for deep learning in nlp"
    - "roberta a robustly optimized bert pretraining approach"
    - "hyperbolic representation learning for fast and efficient neural question answering"

Connected through hyperedge HE-3 (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-4 (induced by source paper "cc2vec distributed representations of code changes"):
  with members:
    - "patchnet hierarchical deep learning based stable patch identification for the linux kernel"
    - "semantic source code models using identifier embeddings"
    - "a neural model for generating natural language summaries of program subroutines"
    - "import2vec learning embeddings for software libraries"
    - "code2vec learning distributed representations of code"
    - "code vectors understanding programs through embedded abstracted symbolic traces"
    - "learning to mine aligned code and natural language pairs from stack overflow"
    - "code2seq generating sequences from structured representations of code"

Connected through hyperedge HE-5 (induced by source paper "joint event extraction along shortest dependency paths using graph convolutional networks"):
  with members:
    - "jointly multiple events extraction via attention based graph information aggregation"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "rapid customization for event extraction"
    - "encoding sentences with graph convolutional networks for semantic role labeling"
    - "semi supervised classification with graph convolutional networks"
    - "joint extraction of events and entities within a document context"
    - "extracting temporal and causal relations between events"

Connected through hyperedge HE-6 (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-7 (induced by source paper "focal visual text attention for memex question answering"):
  with members:
    - "learning a text video embedding from incomplete and heterogeneous data"
    - "differential attention for visual question answering"
    - "vizwiz grand challenge answering visual questions from blind people"
    - "improved fusion of visual and language representations by dense symmetric co attention for visual question answering"
    - "mutan multimodal tucker fusion for visual question answering"
    - "don t just assume look and answer overcoming priors for visual question answering"
    - "ivqa inverse visual question answering"
    - "an analysis of visual question answering algorithms"

Connected through hyperedge HE-8 (induced by source paper "quick and not so dirty unsupervised selection of justification sentences for multi hop question answering"):
  with members:
    - "improving question answering with external knowledge"
    - "understanding dataset design choices for multi hop reasoning"
    - "evidence sentence extraction for machine reading comprehension"
    - "question answering as global reasoning over semantic abstractions"
    - "dream a challenge dataset and models for dialogue based reading comprehension"
    - "repurposing entailment for multi hop question answering tasks"
    - "an interpretable reasoning network for multi relation question answering"
    - "can a suit of armor conduct electricity a new dataset for open book question answering"

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)