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: "on the effect of low frequency terms on neural ir models"
  Abstract: Low-frequency terms are a recurring challenge for information retrieval models, especially neural IR frameworks struggle with adequately capturing infrequently observed words. While these terms are often removed from neural models - mainly as a concession to efficiency demands - they traditionally play an important role in the performance of IR models. In this paper, we analyze the effects of low-frequency terms on the performance and robustness of neural IR models. We conduct controlled experiments on three recent neural IR models, trained on a large-scale passage retrieval collection. We evaluate the neural IR models with various vocabulary sizes for their respective word embeddings, considering different levels of constraints on the available GPU memory. We observe that despite the significant benefits of using larger vocabularies, the performance gap between the vocabularies can be, to a great extent, mitigated by extensive tuning of a related parameter: the number of documents to re-rank. We further investigate the use of subword-token embedding models, and in particular FastText, for neural IR models. Our experiments show that using FastText brings slight improvements to the overall performance of the neural IR models in comparison to models trained on the full vocabulary, while the improvement becomes much more pronounced for queries containing low-frequency terms.

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 "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"
    - "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"

Connected through hyperedge HE-3 (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"
    - "an updated duet model for passage re ranking"
    - "cedr contextualized embeddings for document ranking"
    - "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-4 (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"
    - "an updated duet model for passage re ranking"
    - "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-5 (induced by source paper "tu wien trec deep learning 19 simple contextualization for re ranking"):
  with members:
    - "passage re ranking with bert"
    - "allennlp a deep semantic natural language processing platform"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "end to end neural ad hoc ranking with kernel pooling"
    - "attention is all you need"
    - "co pacrr a context aware neural ir model for ad hoc retrieval"
    - "enriching word vectors with subword information"

Connected through hyperedge HE-6 (induced by source paper "neural ir explorer a content focused tool to explore neural re ranking results"):
  with members:
    - "visualizing and measuring the geometry of bert"
    - "tu wien trec deep learning 19 simple contextualization for re ranking"
    - "let s measure run time extending the ir replicability infrastructure to include performance aspects"
    - "a progressive visual analytics tool for incremental experimental evaluation"
    - "a multiscale visualization of attention in the transformer model"
    - "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)