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: "simple applications of bert for ad hoc document retrieval"
  Abstract: Following recent successes in applying BERT to question answering, we explore simple applications to ad hoc document retrieval. This required confronting the challenge posed by documents that are typically longer than the length of input BERT was designed to handle. We address this issue by applying inference on sentences individually, and then aggregating sentence scores to produce document scores. Experiments on TREC microblog and newswire test collections show that our approach is simple yet effective, as we report the highest average precision on these datasets by neural approaches that we are aware of.

Connected through hyperedge HE-1 (induced by source paper "a deep look into neural ranking models for information retrieval"):
  with members:
    - "learning groupwise scoring functions using deep neural networks"
    - "semeval 2017 task 3 community question answering"
    - "user intent prediction in information seeking conversations"
    - "vitor learning to rank webpages based on visual features"
    - "wikipassageqa a benchmark collection for research on non factoid answer passage retrieval"
    - "anmm ranking short answer texts with attention based neural matching model"
    - "cross domain regularization for neural ranking models using adversarial learning"
    - "learning a deep listwise context model for ranking refinement"

Connected through hyperedge HE-2 (induced by source paper "training neural response selection for task oriented dialogue systems"):
  with members:
    - "passage re ranking with bert"
    - "semeval 2015 task 3 answer selection in community question answering"
    - "a repository of conversational datasets"
    - "multi task deep neural networks for natural language understanding"
    - "bert post training for review reading comprehension and aspect based sentiment analysis"
    - "learning semantic textual similarity from conversations"
    - "improving response selection in multi turn dialogue systems by incorporating domain knowledge"
    - "retrieve and refine improved sequence generation models for dialogue"

Connected through hyperedge HE-3 (induced by source paper "bertscore evaluating text generation with bert"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "fine tune bert for extractive summarization"
    - "alternative weighting schemes for elmo embeddings"
    - "deep reinforcement learning with distributional semantic rewards for abstractive summarization"
    - "pay less attention with lightweight and dynamic convolutions"
    - "unifying human and statistical evaluation for natural language generation"
    - "scibert a pretrained language model for scientific text"
    - "paws paraphrase adversaries from word scrambling"

Connected through hyperedge HE-4 (induced by source paper "curriculum learning strategies for ir an empirical study on conversation response ranking"):
  with members:
    - "bert with history answer embedding for conversational question answering"
    - "passage re ranking with bert"
    - "introducing mantis a novel multi domain information seeking dialogues dataset"
    - "faq retrieval using query question similarity and bert based query answer relevance"
    - "on the power of curriculum learning in training deep networks"
    - "image difficulty curriculum for generative adversarial networks cugan"
    - "a hybrid retrieval generation neural conversation model"
    - "critically examining the neural hype weak baselines and the additivity of effectiveness gains from neural ranking models"

Connected through hyperedge HE-5 (induced by source paper "introducing mantis a novel multi domain information seeking dialogues dataset"):
  with members:
    - "asking clarifying questions in open domain information seeking conversations"
    - "a hybrid retrieval generation neural conversation model"
    - "user intent prediction in information seeking conversations"
    - "interpretation of natural language rules in conversational machine reading"
    - "analyzing and characterizing user intent in information seeking conversations"
    - "integrating planning for task completion dialogue policy learning"
    - "subgoal discovery for hierarchical dialogue policy learning"
    - "learning to ask good questions ranking clarification questions using neural expected value of perfect information"

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"
    - "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"
    - "cedr contextualized embeddings for document ranking"
    - "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 "teaching a new dog old tricks resurrecting multilingual retrieval using zero shot learning"):
  with members:
    - "cross lingual relevance transfer for document retrieval"
    - "deeper text understanding for ir with contextual neural language modeling"
    - "cedr contextualized embeddings for document ranking"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "critically examining the neural hype weak baselines and the additivity of effectiveness gains from neural ranking models"):
  with members:
    - "cedr contextualized embeddings for document ranking"
    - "end to end open domain question answering with"
    - "anmm ranking short answer texts with attention based neural matching model"
    - "multi perspective relevance matching with hierarchical convnets for social media search"
    - "neural vector spaces for unsupervised information retrieval"
    - "end to end neural ad hoc ranking with kernel pooling"
    - "matchzoo a toolkit for deep text matching"
    - "neural models for information retrieval"

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)