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: "investigating the successes and failures of bert for passage re ranking"
  Abstract: The bidirectional encoder representations from transformers (BERT) model has recently advanced the state-of-the-art in passage re-ranking. In this paper, we analyze the results produced by a fine-tuned BERT model to better understand the reasons behind such substantial improvements. To this aim, we focus on the MS MARCO passage re-ranking dataset and provide potential reasons for the successes and failures of BERT for retrieval. In more detail, we empirically study a set of hypotheses and provide additional analysis to explain the successful performance of BERT.

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"
    - "cedr contextualized embeddings for document ranking"

Connected through hyperedge HE-2 (induced by source paper "on identifiability in transformers"):
  with members:
    - "attention is not not explanation"
    - "are sixteen heads really better than one"
    - "analyzing multi head self attention specialized heads do the heavy lifting the rest can be pruned"
    - "attention is not all you need for commonsense reasoning"
    - "visualizing and measuring the geometry of bert"
    - "bert rediscovers the classical nlp pipeline"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "adding interpretable attention to neural translation models improves word alignment"

Connected through hyperedge HE-3 (induced by source paper "multi step entity centric information retrieval for multi hop question answering"):
  with members:
    - "passage re ranking with bert"
    - "a deep look into neural ranking models for information retrieval"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "entity duet neural ranking understanding the role of knowledge graph semantics in neural information retrieval"
    - "the web as a knowledge base for answering complex questions"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "deeptype multilingual entity linking by neural type system evolution"

Connected through hyperedge HE-4 (induced by source paper "asking clarifying questions in open domain information seeking conversations"):
  with members:
    - "answer based adversarial training for generating clarification questions"
    - "a deep look into neural ranking models for information retrieval"
    - "target apps selection towards a unified search framework for mobile devices"
    - "learning to ask questions in open domain conversational systems with typed decoders"
    - "transfer learning for context aware question matching in information seeking conversations in e commerce"
    - "analyzing and characterizing user intent in information seeking conversations"
    - "learning to ask good questions ranking clarification questions using neural expected value of perfect information"
    - "coqa a conversational question answering challenge"

Connected through hyperedge HE-5 (induced by source paper "antique a non factoid question answering benchmark"):
  with members:
    - "passage re ranking with bert"
    - "semeval 2017 task 3 community question answering"
    - "wikipassageqa a benchmark collection for research on non factoid answer passage retrieval"
    - "anmm ranking short answer texts with attention based neural matching model"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a deep relevance matching model for ad hoc retrieval"
    - "squad 100 000 questions for machine comprehension of text"
    - "ms marco a human generated machine reading comprehension dataset"

Connected through hyperedge HE-6 (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"
    - "understanding the behaviors of bert in 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)