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: "critically examining the neural hype weak baselines and the additivity of effectiveness gains from neural ranking models"
  Abstract: Is neural IR mostly hype? In a recent SIGIR Forum article, Lin expressed skepticism that neural ranking models were actually improving ad hoc retrieval effectiveness in limited data scenarios. He provided anecdotal evidence that authors of neural IR papers demonstrate "wins" by comparing against weak baselines. This paper provides a rigorous evaluation of those claims in two ways: First, we conducted a meta-analysis of papers that have reported experimental results on the TREC Robust04 test collection. We do not find evidence of an upward trend in effectiveness over time. In fact, the best reported results are from a decade ago and no recent neural approach comes close. Second, we applied five recent neural models to rerank the strong baselines that Lin used to make his arguments. A significant improvement was observed for one of the models, demonstrating additivity in gains. While there appears to be merit to neural IR approaches, at least some of the gains reported in the literature appear illusory.

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"
    - "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 "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"
    - "document expansion by query prediction"
    - "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-3 (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"
    - "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-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"
    - "simple applications of bert for ad hoc document retrieval"
    - "a hybrid retrieval generation neural conversation model"

Connected through hyperedge HE-5 (induced by source paper "word class embeddings for multiclass text classification"):
  with members:
    - "enhancing domain word embedding via latent semantic imputation"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "deep learning for sentiment analysis a survey"
    - "from word to sense embeddings a survey on vector representations of meaning"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "joint embedding of words and labels for text classification"
    - "attention is all you need"

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"
    - "simple applications of bert for ad hoc document retrieval"
    - "scibert a pretrained language model for scientific text"
    - "scibert pretrained contextualized embeddings for scientific text"
    - "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 "wikir a python toolkit for building a large scale wikipedia based english information retrieval dataset"):
  with members:
    - "matchzoo a learning practicing and developing system for neural text matching"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "a deep look into neural ranking models for information retrieval"
    - "modeling diverse relevance patterns in ad hoc retrieval"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "deeprank a new deep architecture for relevance ranking in information retrieval"
    - "end to end neural ad hoc ranking with kernel pooling"
    - "neural ranking models with weak supervision"

Connected through hyperedge HE-8 (induced by source paper "discourse level factors for sentence deletion in text simplification"):
  with members:
    - "editnts an neural programmer interpreter model for sentence simplification through explicit editing"
    - "a word complexity lexicon and a neural readability ranking model for lexical simplification"
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "sentence simplification with memory augmented neural networks"
    - "simple and effective text simplification using semantic and neural methods"
    - "sentence simplification with deep reinforcement learning"
    - "learning based single document summarization with compression and anaphoricity constraints"
    - "unsupervised sentence simplification using deep semantics"

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)