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: "cedr contextualized embeddings for document ranking"
  Abstract: Although considerable attention has been given to neural ranking architectures recently, far less attention has been paid to the term representations that are used as input to these models. In this work, we investigate how two pretrained contextualized language models (ELMo and BERT) can be utilized for ad-hoc document ranking. Through experiments on TREC benchmarks, we find that several ex-sting neural ranking architectures can benefit from the additional context provided by contextualized language models. Furthermore, we propose a joint approach that incorporates BERT's classification vector into existing neural models and show that it outperforms state-of-the-art ad-hoc ranking baselines. We call this joint approach CEDR (Contextualized Embeddings for Document Ranking). We also address practical challenges in using these models for ranking, including the maximum input length imposed by BERT and runtime performance impacts of contextualized language models.

Connected through hyperedge HE-1 (induced by source paper "selective weak supervision for neural information retrieval"):
  with members:
    - "latent retrieval for weakly supervised open domain question answering"
    - "passage re ranking with bert"
    - "generic intent representation in web search"
    - "document expansion by query prediction"
    - "critically examining the neural hype weak baselines and the additivity of effectiveness gains from neural ranking models"
    - "deeper text understanding for ir with contextual neural language modeling"
    - "understanding the behaviors of bert in ranking"
    - "investigating the successes and failures of bert for passage re ranking"

Connected through hyperedge HE-2 (induced by source paper "interpretable time budget constrained contextualization for re ranking"):
  with members:
    - "analyzing multi head self attention specialized heads do the heavy lifting the rest can be pruned"
    - "passage re ranking with bert"
    - "a study on the interpretability of neural retrieval models using deepshap"
    - "document expansion by query prediction"
    - "incorporating query term independence assumption for efficient retrieval and ranking using deep neural networks"
    - "an updated duet model for passage re ranking"
    - "let s measure run time extending the ir replicability infrastructure to include performance aspects"
    - "xlnet generalized autoregressive pretraining for language understanding"

Connected through hyperedge HE-3 (induced by source paper "document ranking with a pretrained sequence to sequence model"):
  with members:
    - "scaling laws for neural language models"
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "multi stage document ranking with bert"
    - "passage re ranking with bert"
    - "document expansion by query prediction"
    - "critically examining the neural hype weak baselines and the additivity of effectiveness gains from neural ranking models"
    - "show your work improved reporting of experimental results"
    - "zero shot text classification with generative language models"

Connected through hyperedge HE-4 (induced by source paper "ranking significant discrepancies in clinical reports"):
  with members:
    - "end to end neural ad hoc ranking with kernel pooling"
    - "neural ranking models with weak supervision"
    - "pacrr a position aware neural ir model for relevance matching"
    - "learning generic sentence representations using convolutional neural networks"

Connected through hyperedge HE-5 (induced by source paper "kernel graph attention network for fact verification"):
  with members:
    - "several experiments on investigating pretraining and knowledge enhanced models for natural language inference"
    - "bert for evidence retrieval and claim verification"
    - "reasoning over semantic level graph for fact checking"
    - "gear graph based evidence aggregating and reasoning for fact verification"
    - "understanding attention and generalization in graph neural networks"
    - "deeper text understanding for ir with contextual neural language modeling"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "what does bert look at an analysis of bert s attention"

Connected through hyperedge HE-6 (induced by source paper "multi stage document ranking with bert"):
  with members:
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "passage re ranking with bert"
    - "simple applications of bert for ad hoc document retrieval"
    - "scibert a pretrained language model for scientific text"
    - "scibert pretrained contextualized embeddings for scientific text"
    - "critically examining the neural hype weak baselines and the additivity of effectiveness gains from neural ranking models"
    - "end to end open domain question answering with"
    - "query driven algorithm selection in early stage retrieval"

Connected through hyperedge HE-7 (induced by source paper "let s measure run time extending the ir replicability infrastructure to include performance aspects"):
  with members:
    - "analyzing multi head self attention specialized heads do the heavy lifting the rest can be pruned"
    - "passage re ranking with bert"
    - "document expansion by query prediction"
    - "an updated duet model for passage re ranking"
    - "on the effect of low frequency terms on neural ir models"
    - "allennlp a deep semantic natural language processing platform"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "end to end neural ad hoc ranking with kernel pooling"

Connected through hyperedge HE-8 (induced by source paper "teaching a new dog old tricks resurrecting multilingual retrieval using zero shot learning"):
  with members:
    - "cross lingual relevance transfer for document retrieval"
    - "simple applications of bert for ad hoc document retrieval"
    - "deeper text understanding for ir with contextual neural language modeling"
    - "cross lingual language model pretraining"
    - "unsupervised cross lingual information retrieval using monolingual data only"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "cross lingual transfer learning for multilingual task oriented dialog"
    - "end to end neural ad hoc ranking with kernel pooling"

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)