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: "document expansion by query prediction"
  Abstract: One technique to improve the retrieval effectiveness of a search engine is to expand documents with terms that are related or representative of the documents' content.From the perspective of a question answering system, this might comprise questions the document can potentially answer. Following this observation, we propose a simple method that predicts which queries will be issued for a given document and then expands it with those predictions with a vanilla sequence-to-sequence model, trained using datasets consisting of pairs of query and relevant documents. By combining our method with a highly-effective re-ranking component, we achieve the state of the art in two retrieval tasks. In a latency-critical regime, retrieval results alone (without re-ranking) approach the effectiveness of more computationally expensive neural re-rankers but are much faster.

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"
    - "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"
    - "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"
    - "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"
    - "cedr contextualized embeddings for document ranking"
    - "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"
    - "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"
    - "cedr contextualized embeddings for document ranking"

Connected through hyperedge HE-4 (induced by source paper "overview of the trec 2019 deep learning track"):
  with members:
    - "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"
    - "an updated duet model for passage re ranking"
    - "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-5 (induced by source paper "answering complex open domain questions through iterative query generation"):
  with members:
    - "coarse grain fine grain coattention network for multi evidence question answering"
    - "compositional questions do not necessitate multi hop reasoning"
    - "cognitive graph for multi hop reading comprehension at scale"
    - "multi hop paragraph retrieval for open domain question answering"
    - "understanding dataset design choices for multi hop reasoning"
    - "multi hop reading comprehension through question decomposition and rescoring"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "the web as a knowledge base for answering complex questions"

Connected through hyperedge HE-6 (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"
    - "an updated duet model for passage re ranking"
    - "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-7 (induced by source paper "conclusion supplement answer generation for non factoid questions"):
  with members:
    - "multi passage machine reading comprehension with cross passage answer verification"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "stochastic answer networks for natural language inference"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"
    - "attention is all you need"
    - "s net from answer extraction to answer generation for machine reading comprehension"
    - "deep reinforcement learning for dialogue generation"
    - "review networks for caption generation"

Connected through hyperedge HE-8 (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"
    - "deeper text understanding for ir with contextual neural language modeling"
    - "an updated duet model for passage re ranking"
    - "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"

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)