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: "understanding the behaviors of bert in ranking"
  Abstract: This paper studies the performances and behaviors of BERT in ranking tasks. We explore several different ways to leverage the pre-trained BERT and fine-tune it on two ranking tasks: MS MARCO passage reranking and TREC Web Track ad hoc document ranking. Experimental results on MS MARCO demonstrate the strong effectiveness of BERT in question-answering focused passage ranking tasks, as well as the fact that BERT is a strong interaction-based seq2seq matching model. Experimental results on TREC show the gaps between the BERT pre-trained on surrounding contexts and the needs of ad hoc document ranking. Analyses illustrate how BERT allocates its attentions between query-document tokens in its Transformer layers, how it prefers semantic matches between paraphrase tokens, and how that differs with the soft match patterns learned by a click-trained neural ranker.

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"
    - "cedr contextualized embeddings for document ranking"
    - "investigating the successes and failures of bert for passage re ranking"

Connected through hyperedge HE-2 (induced by source paper "a study on efficiency accuracy and document structure for answer sentence selection"):
  with members:
    - "passage re ranking with bert"
    - "tanda transfer and adapt pre trained transformer models for answer sentence selection"
    - "a gated self attention memory network for answer selection"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "energy and policy considerations for deep learning in nlp"
    - "roberta a robustly optimized bert pretraining approach"
    - "hyperbolic representation learning for fast and efficient neural question answering"
    - "glue a multi task benchmark and analysis platform for natural language understanding"

Connected through hyperedge HE-3 (induced by source paper "how does bert answer questions a layer wise analysis of transformer representations"):
  with members:
    - "assessing bert s syntactic abilities"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "attention is not explanation"
    - "transformer xl attentive language models beyond a fixed length context"
    - "universal transformers"
    - "linguistic knowledge and transferability of contextual representations"
    - "visual interpretability for deep learning a survey"
    - "the natural language decathlon multitask learning as question answering"

Connected through hyperedge HE-4 (induced by source paper "sentence bert sentence embeddings using siamese bert networks"):
  with members:
    - "classification and clustering of arguments with contextualized word embeddings"
    - "on measuring social biases in sentence encoders"
    - "real time inference in multi sentence tasks with deep pretrained transformers"
    - "bertscore evaluating text generation with bert"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "roberta a robustly optimized bert pretraining approach"
    - "learning semantic textual similarity from conversations"
    - "why comparing single performance scores does not allow to draw conclusions about machine learning approaches"

Connected through hyperedge HE-5 (induced by source paper "revisit semantic representation and tree search for similar question retrieval"):
  with members:
    - "passage ranking with weak supervision"
    - "joint optimization of tree based index and deep model for recommender systems"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "roberta a robustly optimized bert pretraining approach"
    - "a deep look into neural ranking models for information retrieval"
    - "universal sentence encoder"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "supervised learning of universal sentence representations from natural language inference data"

Connected through hyperedge HE-6 (induced by source paper "a neural document language modeling framework for spoken document retrieval"):
  with members:
    - "fine tune bert for extractive summarization"
    - "simple applications of bert for ad hoc document retrieval"
    - "albert a lite bert for self supervised learning of language representations"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "roberta a robustly optimized bert pretraining approach"
    - "deep contextualized word representations"
    - "attention is all you need"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-7 (induced by source paper "a study of bert for non factoid question answering under passage length constraints"):
  with members:
    - "multi task deep neural networks for natural language understanding"
    - "an updated duet model for passage re ranking"
    - "investigating the successes and failures of bert for passage re ranking"
    - "end to end open domain question answering with"
    - "wikipassageqa a benchmark collection for research on non factoid answer passage retrieval"
    - "attention is all you need"
    - "lstm based deep learning models for non factoid answer selection"

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)