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: "sentence pair scoring towards unified framework for text comprehension"
  Abstract: We review the task of Sentence Pair Scoring, popular in the literature in various forms - viewed as Answer Sentence Selection, Semantic Text Scoring, Next Utterance Ranking, Recognizing Textual Entailment, Paraphrasing or e.g. a component of Memory Networks. #R##N#We argue that all such tasks are similar from the model perspective and propose new baselines by comparing the performance of common IR metrics and popular convolutional, recurrent and attention-based neural models across many Sentence Pair Scoring tasks and datasets. We discuss the problem of evaluating randomized models, propose a statistically grounded methodology, and attempt to improve comparisons by releasing new datasets that are much harder than some of the currently used well explored benchmarks. We introduce a unified open source software framework with easily pluggable models and tasks, which enables us to experiment with multi-task reusability of trained sentence model. We set a new state-of-art in performance on the Ubuntu Dialogue dataset.

Connected through hyperedge HE-1 (induced by source paper "training neural response selection for task oriented dialogue systems"):
  with members:
    - "passage re ranking with bert"
    - "semeval 2015 task 3 answer selection in community question answering"
    - "simple applications of bert for ad hoc document retrieval"
    - "a repository of conversational datasets"
    - "multi task deep neural networks for natural language understanding"
    - "bert post training for review reading comprehension and aspect based sentiment analysis"
    - "learning semantic textual similarity from conversations"
    - "improving response selection in multi turn dialogue systems by incorporating domain knowledge"

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 "composition of sentence embeddings lessons from statistical relational learning"):
  with members:
    - "breaking nli systems with sentences that require simple lexical inferences"
    - "pair2vec compositional word pair embeddings for cross sentence inference"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "an efficient framework for learning sentence representations"
    - "evaluating compositionality in sentence embeddings"
    - "natural language inference over interaction space"
    - "evaluating vector space models of analogy"
    - "supervised learning of universal sentence representations from natural language inference data"

Connected through hyperedge HE-4 (induced by source paper "selection bias explorations and debias methods for natural language sentence matching datasets"):
  with members:
    - "sentence modeling via multiple word embeddings and multi level comparison for semantic textual similarity"
    - "annotation artifacts in natural language inference data"
    - "what makes reading comprehension questions easier"
    - "hypothesis only baselines in natural language inference"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "baseline needs more love on simple word embedding based models and associated pooling mechanisms"
    - "semantic sentence matching with densely connected recurrent and co attentive information"
    - "natural language inference over interaction space"

Connected through hyperedge HE-5 (induced by source paper "triplenet triple attention network for multi turn response selection in retrieval based chatbots"):
  with members:
    - "convolutional spatial attention model for reading comprehension with multiple choice questions"
    - "deep contextualized word representations"
    - "a structured self attentive sentence embedding"
    - "bidirectional attention flow for machine comprehension"
    - "on the evaluation of dialogue systems with next utterance classification"
    - "sequential matching network a new architecture for multi turn response selection in retrieval based chatbots"
    - "match srnn modeling the recursive matching structure with spatial rnn"
    - "syntax based deep matching of short texts"

Connected through hyperedge HE-6 (induced by source paper "enhance word representation for out of vocabulary on ubuntu dialogue corpus"):
  with members:
    - "bilateral multi perspective matching for natural language sentences"
    - "a decomposable attention model for natural language inference"
    - "bidirectional attention flow for machine comprehension"
    - "conceptnet 5 5 an open multilingual graph of general knowledge"
    - "enhanced lstm for natural language inference"
    - "enriching word vectors with subword information"
    - "multi task cross lingual sequence tagging from scratch"
    - "sequential matching network a new architecture for multi turn response selection in retrieval based chatbots"

Connected through hyperedge HE-7 (induced by source paper "improving response selection in multi turn dialogue systems by incorporating domain knowledge"):
  with members:
    - "improving retrieval modeling using cross convolution networks and multi frequency word embedding"
    - "learning to compute word embeddings on the fly"
    - "enhance word representation for out of vocabulary on ubuntu dialogue corpus"
    - "towards end to end learning for dialog state tracking and management using deep reinforcement learning"
    - "topic augmented neural network for short text conversation"
    - "enriching word vectors with subword information"
    - "sequential matching network a new architecture for multi turn response selection in retrieval based chatbots"
    - "learning end to end goal oriented dialog"

Connected through hyperedge HE-8 (induced by source paper "improving retrieval modeling using cross convolution networks and multi frequency word embedding"):
  with members:
    - "self guiding multimodal lstm when we do not have a perfect training dataset for image captioning"
    - "on the evaluation of dialogue systems with next utterance classification"
    - "sequential matching network a new architecture for multi turn response selection in retrieval based chatbots"
    - "incorporating loose structured knowledge into conversation modeling via recall gate lstm"
    - "syntax based deep matching of short texts"
    - "improved deep learning baselines for ubuntu corpus dialogs"
    - "the ubuntu dialogue corpus a large dataset for research in unstructured multi turn dialogue systems"
    - "a neural network approach to context sensitive generation of conversational responses"

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)