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: "neural models of factuality"
  Abstract: We present two neural models for event factuality prediction, which yield significant performance gains over previous models on three event factuality datasets: FactBank, UW, and MEANTIME. We also present a substantial expansion of the It Happened portion of the Universal Decompositional Semantics dataset, yielding the largest event factuality dataset to date. We report model results on this extended factuality dataset as well.

Connected through hyperedge HE-1 (induced by source paper "commonsense reasoning for natural language understanding a survey of benchmarks resources and approaches"):
  with members:
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "multi task deep neural networks for natural language understanding"
    - "explicit bias discovery in visual question answering models"
    - "modeling naive psychology of characters in simple commonsense stories"
    - "from recognition to cognition visual commonsense reasoning"
    - "annotation artifacts in natural language inference data"
    - "can a suit of armor conduct electricity a new dataset for open book question answering"
    - "gender bias in coreference resolution"

Connected through hyperedge HE-2 (induced by source paper "linguistic knowledge and transferability of contextual representations"):
  with members:
    - "to tune or not to tune adapting pretrained representations to diverse tasks"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "cross lingual alignment of contextual word embeddings with applications to zero shot dependency parsing"
    - "what can linguistics and deep learning contribute to each other"
    - "what do rnn language models learn about filler gap dependencies"
    - "what s going on in neural constituency parsers an analysis"
    - "language modeling teaches you more syntax than translation does lessons learned through auxiliary task analysis"
    - "why self attention a targeted evaluation of neural machine translation architectures"

Connected through hyperedge HE-3 (induced by source paper "right for the wrong reasons diagnosing syntactic heuristics in natural language inference"):
  with members:
    - "assessing bert s syntactic abilities"
    - "human vs muppet a conservative estimate of human performance on the glue benchmark"
    - "non entailed subsequences as a challenge for natural language inference"
    - "annotation artifacts in natural language inference data"
    - "assessing composition in sentence vector representations"
    - "breaking nli systems with sentences that require simple lexical inferences"
    - "collecting diverse natural language inference problems for sentence representation evaluation"
    - "teaching syntax by adversarial distraction"

Connected through hyperedge HE-4 (induced by source paper "automatically neutralizing subjective bias in text"):
  with members:
    - "towards controlled transformation of sentiment in sentences"
    - "black is to criminal as caucasian is to police detecting and removing multiclass bias in word embeddings"
    - "evaluating style transfer for text"
    - "identifying and reducing gender bias in word level language models"
    - "understanding undesirable word embedding associations"
    - "approaching neural grammatical error correction as a low resource machine translation task"
    - "wikiatomicedits a multilingual corpus of wikipedia edits for modeling language and discourse"
    - "delete retrieve generate a simple approach to sentiment and style transfer"

Connected through hyperedge HE-5 (induced by source paper "shallow syntax in deep water"):
  with members:
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "linguistic knowledge and transferability of contextual representations"
    - "comprehensive supersense disambiguation of english prepositions and possessives"
    - "allennlp a deep semantic natural language processing platform"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "deep rnns encode soft hierarchical syntax"
    - "universal language model fine tuning for text classification"

Connected through hyperedge HE-6 (induced by source paper "harnessing the richness of the linguistic signal in predicting pragmatic inferences"):
  with members:
    - "attention is not not explanation"
    - "what syntactic structures block dependencies in rnn language models"
    - "attention is not explanation"
    - "huggingface s transformers state of the art natural language processing"
    - "attention interpretability across nlp tasks"
    - "neural language models as psycholinguistic subjects representations of syntactic state"
    - "linguistic knowledge and transferability of contextual representations"
    - "allennlp a deep semantic natural language processing platform"

Connected through hyperedge HE-7 (induced by source paper "uncertain natural language inference"):
  with members:
    - "learning to rank for plausible plausibility"
    - "annotation artifacts in natural language inference data"
    - "collecting diverse natural language inference problems for sentence representation evaluation"
    - "hypothesis only baselines in natural language inference"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "performance impact caused by hidden bias of training data for recognizing textual entailment"
    - "efficient online scalar annotation with bounded support"
    - "understanding deep learning performance through an examination of test set difficulty a psychometric case study"

Connected through hyperedge HE-8 (induced by source paper "fine grained temporal relation extraction"):
  with members:
    - "joint reasoning for temporal and causal relations"
    - "decomposing generalization models of generic habitual and episodic statements"
    - "a structured learning approach to temporal relation extraction"
    - "deep contextualized word representations"
    - "lexicosyntactic inference in neural models"
    - "temporal information extraction by predicting relative time lines"
    - "attention is all you need"
    - "effective approaches to attention based neural machine translation"

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.CL(Computation and Language)