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: "tagme on the fly annotation of short text fragments by wikipedia entities"
  Abstract: In this paper we address the problem of accurately and efficiently cross-referencing text fragments with Wikipedia pages, in a way that structured knowledge is provided about the (unstructured) input text by resolving synonymy and polysemy. We take inspiration from the invited talk of Chakrabarti at WSDM 2010, and extend his proposed scenario from the annotation of entire documents to the annotation of short texts, such as snippets of search-engine results, tweets, news, etc.. These short and poorly composed texts pose new challenges in terms of efficiency and effectiveness of the annotation process, that we address by proposing 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 significantly outperforms state-of-the-art algorithms [Milne and Witten 2008, Chakrabarty et al. 2009] when they are adapted to work on short texts, and surprisingly, it results competitive (if not superior!) on long texts with the "plus" of being faster.

Connected through hyperedge HE-1 (induced by source paper "towards quantifying the distance between opinions"):
  with members:
    - "deep learning for sentiment analysis a survey"
    - "viewpoint discovery and understanding in social networks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "a simple but tough to beat baseline for the fake news challenge stance detection task"
    - "globally normalized transition based neural networks"
    - "distributed representations of sentences and documents"
    - "opinion mining and analysis a survey"

Connected through hyperedge HE-2 (induced by source paper "the stem ecr dataset grounding scientific entity references in stem scholarly content to authoritative encyclopedic and lexicographic sources"):
  with members:
    - "scibert a pretrained language model for scientific text"
    - "open research knowledge graph next generation infrastructure for semantic scholarly knowledge"
    - "multi task identification of entities relations and coreference for scientific knowledge graph construction"
    - "allennlp a deep semantic natural language processing platform"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "construction of the literature graph in semantic scholar"
    - "semeval 2017 task 10 scienceie extracting keyphrases and relations from scientific publications"
    - "scientific information extraction with semi supervised neural tagging"

Connected through hyperedge HE-3 (induced by source paper "knowledge enhanced contextual word representations"):
  with members:
    - "barack s wife hillary using knowledge graphs for fact aware language modeling"
    - "leveraging knowledge bases in lstms for improving machine reading"
    - "wic the word in context dataset for evaluating context sensitive meaning representations"
    - "matching the blanks distributional similarity for relation learning"
    - "ernie enhanced language representation with informative entities"
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "tucker tensor factorization for knowledge graph completion"
    - "simple bert models for relation extraction and semantic role labeling"

Connected through hyperedge HE-4 (induced by source paper "ernie enhanced language representation with informative entities"):
  with members:
    - "ernie enhanced representation through knowledge integration"
    - "improving question answering by commonsense based pre training"
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "fewrel a large scale supervised few shot relation classification dataset with state of the art evaluation"
    - "knowledgeable reader enhancing cloze style reading comprehension with external commonsense knowledge"
    - "joint representation learning of cross lingual words and entities via attentive distant supervision"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "ultra fine entity typing"

Connected through hyperedge HE-5 (induced by source paper "a review of keyphrase extraction"):
  with members:
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "keyphrase generation with correlation constraints"
    - "learning feature representations for keyphrase extraction"
    - "semi supervised learning for neural keyphrase generation"
    - "simple unsupervised keyphrase extraction using sentence embeddings"
    - "joint entity recognition and relation extraction as a multi head selection problem"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "unsupervised learning of sentence embeddings using compositional n gram features"

Connected through hyperedge HE-6 (induced by source paper "docred a large scale document level relation extraction dataset"):
  with members:
    - "a walk based model on entity graphs for relation extraction"
    - "simultaneously self attending to all mentions for full abstract biological relation extraction"
    - "fewrel a large scale supervised few shot relation classification dataset with state of the art evaluation"
    - "improving the transformer translation model with document level context"
    - "robust distant supervision relation extraction via deep reinforcement learning"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "fever a large scale dataset for fact extraction and verification"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-7 (induced by source paper "look before you hop conversational question answering over knowledge graphs using judicious context expansion"):
  with members:
    - "answering complex questions by joining multi document evidence with quasi knowledge graphs"
    - "interpretation of natural language rules in conversational machine reading"
    - "comqa a community sourced dataset for complex factoid question answering with paraphrase clusters"
    - "the web as a knowledge base for answering complex questions"
    - "coqa a conversational question answering challenge"
    - "open domain question answering using early fusion of knowledge bases and text"
    - "complex sequential question answering towards learning to converse over linked question answer pairs with a knowledge graph"
    - "learning to paraphrase for question answering"

Connected through hyperedge HE-8 (induced by source paper "learning to ask question based sequential bayesian product search"):
  with members:
    - "technology assisted reviews finding the last few relevant documents by asking yes no questions to reviewers"
    - "playing 20 question game with policy based reinforcement learning"
    - "attentive long short term preference modeling for personalized product search"
    - "learning to ask knowledge acquisition via 20 questions"
    - "joint modeling and optimization of search and recommendation"
    - "conversational recommender system"
    - "word entity duet representations for document ranking"
    - "learning latent vector spaces for product search"

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)