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: "learning to rank question answer pairs using hierarchical recurrent encoder with latent topic clustering"
  Abstract: In this paper, we propose a novel end-to-end neural architecture for ranking candidate answers, that adapts a hierarchical recurrent neural network and a latent topic clustering module. With our proposed model, a text is encoded to a vector representation from an word-level to a chunk-level to effectively capture the entire meaning. In particular, by adapting the hierarchical structure, our model shows very small performance degradations in longer text comprehension while other state-of-the-art recurrent neural network models suffer from it. Additionally, the latent topic clustering module extracts semantic information from target samples. This clustering module is useful for any text related tasks by allowing each data sample to find its nearest topic cluster, thus helping the neural network model analyze the entire data. We evaluate our models on the Ubuntu Dialogue Corpus and consumer electronic domain question answering dataset, which is related to Samsung products. The proposed model shows state-of-the-art results for ranking question-answer pairs.

Connected through hyperedge HE-1 (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-2 (induced by source paper "interactive matching network for multi turn response selection in retrieval based chatbots"):
  with members:
    - "building sequential inference models for end to end response selection"
    - "deep contextualized word representations"
    - "modeling multi turn conversation with deep utterance aggregation"
    - "enhance word representation for out of vocabulary on ubuntu dialogue corpus"
    - "end to end neural coreference resolution"
    - "a survey on dialogue systems recent advances and new frontiers"
    - "augmenting end to end dialog systems with commonsense knowledge"
    - "bilateral multi perspective matching for natural language sentences"

Connected through hyperedge HE-3 (induced by source paper "utterance to utterance interactive matching network for multi turn response selection in retrieval based chatbots"):
  with members:
    - "exploiting persona information for diverse generation of conversational responses"
    - "dually interactive matching network for personalized response selection in retrieval based chatbots"
    - "interactive matching network for multi turn response selection in retrieval based chatbots"
    - "building sequential inference models for end to end response selection"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "modeling multi turn conversation with deep utterance aggregation"
    - "end to end neural coreference resolution"
    - "a survey on dialogue systems recent advances and new frontiers"

Connected through hyperedge HE-4 (induced by source paper "joint learning of answer selection and answer summary generation in community question answering"):
  with members:
    - "semeval 2017 task 3 community question answering"
    - "wikipassageqa a benchmark collection for research on non factoid answer passage retrieval"
    - "joint training of candidate extraction and answer selection for reading comprehension"
    - "duorc towards complex language understanding with paraphrased reading comprehension"
    - "neural document summarization by jointly learning to score and select sentences"
    - "wikihow a large scale text summarization dataset"
    - "joint multitask learning for community question answering using task specific embeddings"
    - "multi task learning with multi view attention for answer selection and knowledge base question answering"

Connected through hyperedge HE-5 (induced by source paper "poly encoders transformer architectures and pre training strategies for fast and accurate multi sentence scoring"):
  with members:
    - "the second conversational intelligence challenge convai2"
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "acute eval improved dialogue evaluation with optimized questions and multi turn comparisons"
    - "learning to speak and act in a fantasy text adventure game"
    - "sequential attention based network for noetic end to end response selection"
    - "training millions of personalized dialogue agents"
    - "wizard of wikipedia knowledge powered conversational agents"
    - "personalizing dialogue agents i have a dog do you have pets too"

Connected through hyperedge HE-6 (induced by source paper "semantic relation classification via bidirectional lstm networks with entity aware attention using latent entity typing"):
  with members:
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "attention is all you need"
    - "deep semantic role labeling with self attention"
    - "disan directional self attention network for rnn cnn free language understanding"
    - "improved relation classification by deep recurrent neural networks with data augmentation"
    - "classifying relations by ranking with convolutional neural networks"
    - "semantic relation classification via convolutional neural networks with simple negative sampling"
    - "a dependency based neural network for relation classification"

Connected through hyperedge HE-7 (induced by source paper "detecting incongruity between news headline and body text via a deep hierarchical encoder"):
  with members:
    - "sampling the news producers a large news and feature data set for the study of the complex media landscape"
    - "a simple but tough to beat baseline for the fake news challenge stance detection task"
    - "diving deep into clickbaits who use them to what extents in which topics with what effects"
    - "learning to identify ambiguous and misleading news headlines"
    - "bilateral multi perspective matching for natural language sentences"
    - "stop clickbait detecting and preventing clickbaits in online news media"
    - "xgboost a scalable tree boosting system"
    - "breaking the news first impressions matter on online news"

Connected through hyperedge HE-8 (induced by source paper "comparative studies of detecting abusive language on twitter"):
  with members:
    - "large scale crowdsourcing and characterization of twitter abusive behavior"
    - "mean birds detecting aggression and bullying on twitter"
    - "deep learning for hate speech detection in tweets"
    - "modeling rich contexts for sentiment classification with lstm"
    - "adam a method for stochastic optimization"
    - "distributed representations of sentences and documents"
    - "convolutional neural networks for sentence classification"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

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)