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: "an updated duet model for passage re ranking"
  Abstract: We propose several small modifications to Duet---a deep neural ranking model---and evaluate the updated model on the MS MARCO passage ranking task. We report significant improvements from the proposed changes based on an ablation study.

Connected through hyperedge HE-1 (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"
    - "let s measure run time extending the ir replicability infrastructure to include performance aspects"
    - "cedr contextualized embeddings for document ranking"
    - "xlnet generalized autoregressive pretraining for language understanding"

Connected through hyperedge HE-2 (induced by source paper "overview of the trec 2019 deep learning track"):
  with members:
    - "document expansion by query prediction"
    - "incorporating query term independence assumption for efficient retrieval and ranking using deep neural networks"
    - "critically examining the neural hype weak baselines and the additivity of effectiveness gains from neural ranking models"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "neural ranking models with weak supervision"
    - "learning to match using local and distributed representations of text for web search"
    - "ms marco a human generated machine reading comprehension dataset"
    - "bag of tricks for efficient text classification"

Connected through hyperedge HE-3 (induced by source paper "large scale question answering using tourism data"):
  with members:
    - "eli5 long form question answering"
    - "can a suit of armor conduct electricity a new dataset for open book question answering"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "know what you don t know unanswerable questions for squad"
    - "coqa a conversational question answering challenge"
    - "universal sentence encoder"
    - "deep relevance ranking using enhanced document query interactions"
    - "quac question answering in context"

Connected through hyperedge HE-4 (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"
    - "cedr contextualized embeddings for document 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-5 (induced by source paper "learning representations and agents for information retrieval"):
  with members:
    - "hierarchical neural story generation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "evidence aggregation for answer re ranking in open domain question answering"
    - "r 3 reinforced reader ranker for open domain question answering"
    - "improving exploration in evolution strategies for deep reinforcement learning via a population of novelty seeking agents"
    - "a deep reinforcement learning chatbot"
    - "bidirectional attention flow for machine comprehension"
    - "squad 100 000 questions for machine comprehension of text"

Connected through hyperedge HE-6 (induced by source paper "duet at trec 2019 deep learning track"):
  with members:
    - "overview of the trec 2019 deep learning track"
    - "passage re ranking with bert"
    - "incorporating query term independence assumption for efficient retrieval and ranking using deep neural networks"
    - "cross domain regularization for neural ranking models using adversarial learning"
    - "neural ranking models with multiple document fields"
    - "benchmark for complex answer retrieval"
    - "learning to match using local and distributed representations of text for web search"
    - "a dual embedding space model for document ranking"

Connected through hyperedge HE-7 (induced by source paper "context aware sentence passage term importance estimation for first stage retrieval"):
  with members:
    - "passage re ranking with bert"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "document expansion by query prediction"
    - "deeper text understanding for ir with contextual neural language modeling"
    - "on the effect of low frequency terms on neural ir models"
    - "deep contextualized word representations"
    - "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 "incorporating query term independence assumption for efficient retrieval and ranking using deep neural networks"):
  with members:
    - "passage re ranking with bert"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "ann benchmarks a benchmarking tool for approximate nearest neighbor algorithms"
    - "end to end neural ad hoc ranking with kernel pooling"
    - "neural ranking models with multiple document fields"
    - "benchmark for complex answer retrieval"
    - "a deep relevance matching model for ad hoc retrieval"
    - "learning to match using local and distributed representations of text for web 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)