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: "word embedding based correlation model for question answer matching"
  Abstract: With the development of community based question answering (Q\&A) services, a large scale of Q\&A archives have been accumulated and are an important information and knowledge resource on the web. Question and answer matching has been attached much importance to for its ability to reuse knowledge stored in these systems: it can be useful in enhancing user experience with recurrent questions. In this paper, we try to improve the matching accuracy by overcoming the lexical gap between question and answer pairs. A Word Embedding based Correlation (WEC) model is proposed by integrating advantages of both the translation model and word embedding, given a random pair of words, WEC can score their co-occurrence probability in Q\&A pairs and it can also leverage the continuity and smoothness of continuous space word representation to deal with new pairs of words that are rare in the training parallel text. An experimental study on Yahoo! Answers dataset and Baidu Zhidao dataset shows this new method's promising potential.

Connected through hyperedge HE-1 (induced by source paper "enhancing clinical concept extraction with contextual embeddings"):
  with members:
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "clinicalbert modeling clinical notes and predicting hospital readmission"
    - "publicly available clinical bert embeddings"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "clinical concept extraction with contextual word embedding"
    - "recurrent neural networks with specialized word embeddings for health domain named entity recognition"
    - "attention is all you need"

Connected through hyperedge HE-2 (induced by source paper "promotion of answer value measurement with domain effects in community question answering systems"):
  with members:
    - "anmm ranking short answer texts with attention based neural matching model"
    - "learning from history and present next item recommendation via discriminatively exploiting user behaviors"
    - "abcnn attention based convolutional neural network for modeling sentence pairs"
    - "convolutional neural network architectures for matching natural language sentences"
    - "a deep architecture for semantic matching with multiple positional sentence representations"
    - "speech recognition with deep recurrent neural networks"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-3 (induced by source paper "machine translation evaluation meets community question answering"):
  with members:
    - "semeval 2016 task 3 community question answering"
    - "semanticz at semeval 2016 task 3 ranking relevant answers in community question answering using semantic similarity based on fine tuned word embeddings"
    - "pairwise neural machine translation evaluation"
    - "semi supervised question retrieval with gated convolutions"
    - "applying deep learning to answer selection a study and an open task"
    - "lstm based deep learning models for non factoid answer selection"

Connected through hyperedge HE-4 (induced by source paper "w rnn news text classification based on a weighted rnn"):
  with members:
    - "fine grained event categorization with heterogeneous graph convolutional networks"

Connected through hyperedge HE-5 (induced by source paper "adversarial training for community question answer selection based on multi scale matching"):
  with members:
    - "semeval 2016 task 3 community question answering"
    - "semanticz at semeval 2016 task 3 ranking relevant answers in community question answering using semantic similarity based on fine tuned word embeddings"
    - "uh prhlt at semeval 2016 task 3 combining lexical and semantic based features for community question answering"
    - "irgan a minimax game for unifying generative and discriminative information retrieval models"
    - "bilateral multi perspective matching for natural language sentences"
    - "deep reinforcement learning for dialogue generation"
    - "seqgan sequence generative adversarial nets with policy gradient"
    - "a decomposable attention model for natural language inference"

Connected through hyperedge HE-6 (induced by source paper "towards understanding and answering multi sentence recommendation questions on tourism"):
  with members:
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"
    - "question answering on knowledge bases and text using universal schema and memory networks"
    - "reading wikipedia to answer open domain questions"
    - "answering complex questions using open information extraction"
    - "newsqa a machine comprehension dataset"
    - "squad 100 000 questions for machine comprehension of text"
    - "science question answering using instructional materials"
    - "question answering on freebase via relation extraction and textual evidence"

Connected through hyperedge HE-7 (induced by source paper "denoising bodies to titles retrieving similar questions with recurrent convolutional models"):
  with members:
    - "semeval 2015 task 3 answer selection in community question answering"
    - "semeval 2016 task 3 community question answering"
    - "a neural attention model for abstractive sentence summarization"
    - "applying deep learning to answer selection a study and an open task"
    - "character aware neural language models"
    - "text understanding from scratch"
    - "semi supervised sequence learning"
    - "reasoning about entailment with neural attention"

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)