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: "fast and accurate annotation of short texts with wikipedia pages"
  Abstract: We address the problem of cross-referencing text fragments with Wikipedia pages, in a way that synonymy and polysemy issues are resolved accurately and efficiently. We take inspiration from a recent flow of work [Cucerzan 2007, Mihalcea and Csomai 2007, Milne and Witten 2008, Chakrabarti et al 2009], and extend their scenario from the annotation of long documents to the annotation of short texts, such as snippets of search-engine results, tweets, news, blogs, etc.. These short and poorly composed texts pose new challenges in terms of efficiency and effectiveness of the annotation process, that we address by designing and engineering TAGME, the first system that performs an accurate and on-the-fly annotation of these short textual fragments. A large set of experiments shows that TAGME outperforms state-of-the-art algorithms when they are adapted to work on short texts and it results fast and competitive on long texts.

Connected through hyperedge HE-1 (induced by source paper "learning to retrieve reasoning paths over wikipedia graph for question answering"):
  with members:
    - "latent retrieval for weakly supervised open domain question answering"
    - "passage re ranking with bert"
    - "do multi hop readers dream of reasoning chains"
    - "pullnet open domain question answering with iterative retrieval on knowledge bases and text"
    - "compositional questions do not necessitate multi hop reasoning"
    - "cognitive graph for multi hop reading comprehension at scale"
    - "multi hop paragraph retrieval for open domain question answering"
    - "retrieve read rerank towards end to end multi document reading comprehension"

Connected through hyperedge HE-2 (induced by source paper "neural attentive bag of entities model for text classification"):
  with members:
    - "neural collective entity linking"
    - "end to end neural entity linking"
    - "studio ousia s quiz bowl question answering system"
    - "baseline needs more love on simple word embedding based models and associated pooling mechanisms"
    - "wikipedia2vec an optimized tool for learning embeddings of words and entities from wikipedia"
    - "representation learning of entities and documents from knowledge base descriptions"
    - "learning distributed representations of texts and entities from knowledge base"
    - "named entity disambiguation for noisy text"

Connected through hyperedge HE-3 (induced by source paper "knowledge guided text retrieval and reading for open domain question answering"):
  with members:
    - "latent retrieval for weakly supervised open domain question answering"
    - "pullnet open domain question answering with iterative retrieval on knowledge bases and text"
    - "compositional questions do not necessitate multi hop reasoning"
    - "a discrete hard em approach for weakly supervised question answering"
    - "multi passage bert a globally normalized bert model for open domain question answering"
    - "a bert baseline for the natural questions"
    - "multi step entity centric information retrieval for multi hop question answering"
    - "knowledgeable reader enhancing cloze style reading comprehension with external commonsense knowledge"

Connected through hyperedge HE-4 (induced by source paper "distantly supervised question parsing"):
  with members:
    - "introduction to neural network based approaches for question answering over knowledge graphs"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "earl joint entity and relation linking for question answering over knowledge graphs"
    - "building a conversational agent overnight with dialogue self play"
    - "a hierarchical framework for relation extraction with reinforcement learning"
    - "practical bayesian optimization in the presence of outliers"
    - "s mart novel tree based structured learning algorithms applied to tweet entity linking"
    - "learning to compose neural networks for question answering"

Connected through hyperedge HE-5 (induced by source paper "towards better social crisis data with hermes hybrid sensing for emergency management system"):
  with members:
    - "a practitioners guide to transfer learning for text classification using convolutional neural networks"
    - "a survey of location prediction on twitter"
    - "rapid classification of crisis related data on social networks using convolutional neural networks"
    - "twitter as a lifeline human annotated twitter corpora for nlp of crisis related messages"
    - "processing social media messages in mass emergency a survey"
    - "distributed representations of words and phrases and their compositionality"
    - "natural language processing almost from scratch"

Connected through hyperedge HE-6 (induced by source paper "wiser a semantic approach for expert finding in academia based on entity linking"):
  with members:
    - "semantic entity retrieval toolkit"
    - "unsupervised efficient and semantic expertise retrieval"
    - "learning latent vector spaces for product search"
    - "learning to rank academic experts in the dblp dataset"
    - "deepwalk online learning of social representations"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-7 (induced by source paper "towards optimisation of collaborative question answering over knowledge graphs"):
  with members:
    - "earl joint entity and relation linking for question answering over knowledge graphs"
    - "natural language aggregate query over rdf data"
    - "no one is perfect analysing the performance of question answering components over the dbpedia knowledge graph"
    - "enriching word vectors with subword information"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-8 (induced by source paper "fast end to end wikification"):
  with members:
    - "what did you mention a large scale mention detection benchmark for spoken and written text"
    - "deep joint entity disambiguation with local neural attention"
    - "evaluating the word expert approach for named entity disambiguation"
    - "joint learning of the embedding of words and entities for named entity disambiguation"

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.IR(Information Retrieval)