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: "analyzing compositionality sensitivity of nli models"
  Abstract: Success in natural language inference (NLI) should require a model to understand both lexical and compositional semantics. However, through adversarial evaluation, we find that several state-of-the-art models with diverse architectures are over-relying on the former and fail to use the latter. Further, this compositionality unawareness is not reflected via standard evaluation on current datasets. We show that removing RNNs in existing models or shuffling input words during training does not induce large performance loss despite the explicit removal of compositional information. Therefore, we propose a compositionality-sensitivity testing setup that analyzes models on natural examples from existing datasets that cannot be solved via lexical features alone (i.e., on which a bag-of-words model gives a high probability to one wrong label), hence revealing the models' actual compositionality awareness. We show that this setup not only highlights the limited compositional ability of current NLI models, but also differentiates model performance based on design, e.g., separating shallow bag-of-words models from deeper, linguistically-grounded tree-based models. Our evaluation setup is an important analysis tool: complementing currently existing adversarial and linguistically driven diagnostic evaluations, and exposing opportunities for future work on evaluating models' compositional understanding.

Connected through hyperedge HE-1 (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-2 (induced by source paper "a logic driven framework for consistency of neural models"):
  with members:
    - "inoculation by fine tuning a method for analyzing challenge datasets"
    - "augmenting neural networks with first order logic"
    - "synthetic qa corpora generation with roundtrip consistency"
    - "be consistent improving procedural text comprehension using label consistency"
    - "a unified model for extractive and abstractive summarization using inconsistency loss"
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "breaking nli systems with sentences that require simple lexical inferences"
    - "glue a multi task benchmark and analysis platform for natural language understanding"

Connected through hyperedge HE-3 (induced by source paper "assessing the benchmarking capacity of machine reading comprehension datasets"):
  with members:
    - "do neural dialog systems use the conversation history effectively an empirical study"
    - "superglue a stickier benchmark for general purpose language understanding systems"
    - "a systematic classification of knowledge reasoning and context within the arc dataset"
    - "annotation artifacts in natural language inference data"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "what makes reading comprehension questions easier"
    - "duorc towards complex language understanding with paraphrased reading comprehension"
    - "know what you don t know unanswerable questions for squad"

Connected through hyperedge HE-4 (induced by source paper "an empirical study of content understanding in conversational question answering"):
  with members:
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "do neural dialog systems use the conversation history effectively an empirical study"
    - "sdnet contextualized attention based deep network for conversational question answering"
    - "a qualitative comparison of coqa squad 2 0 and quac"
    - "coqa a conversational question answering challenge"
    - "drcd a chinese machine reading comprehension dataset"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "how much reading does reading comprehension require a critical investigation of popular benchmarks"

Connected through hyperedge HE-5 (induced by source paper "analyzing machine learned representations a natural language case study"):
  with members:
    - "right for the wrong reasons diagnosing syntactic heuristics in natural language inference"
    - "human few shot learning of compositional instructions"
    - "mutual exclusivity as a challenge for deep neural networks"
    - "breaking nli systems with sentences that require simple lexical inferences"
    - "analysis methods in neural language processing a survey"
    - "relational inductive biases deep learning and graph networks"
    - "deep learning a critical appraisal"
    - "adventure adversarial training for textual entailment with knowledge guided examples"

Connected through hyperedge HE-6 (induced by source paper "improving generalization by incorporating coverage in natural language inference"):
  with members:
    - "right for the wrong reasons diagnosing syntactic heuristics in natural language inference"
    - "inoculation by fine tuning a method for analyzing challenge datasets"
    - "learning and evaluating general linguistic intelligence"
    - "multi task deep neural networks for natural language understanding"
    - "non entailed subsequences as a challenge for natural language inference"
    - "annotation artifacts in natural language inference data"
    - "the natural language decathlon multitask learning as question answering"
    - "breaking nli systems with sentences that require simple lexical inferences"

Connected through hyperedge HE-7 (induced by source paper "adversarial analysis of natural language inference systems"):
  with members:
    - "right for the wrong reasons diagnosing syntactic heuristics in natural language inference"
    - "assessing bert s syntactic abilities"
    - "inoculation by fine tuning a method for analyzing challenge datasets"
    - "multi task deep neural networks for natural language understanding"
    - "annotation artifacts in natural language inference data"
    - "breaking nli systems with sentences that require simple lexical inferences"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "deep probabilistic logic a unifying framework for indirect supervision"

Connected through hyperedge HE-8 (induced by source paper "posing fair generalization tasks for natural language inference"):
  with members:
    - "help a dataset for identifying shortcomings of neural models in monotonicity reasoning"
    - "annotation artifacts in natural language inference data"
    - "breaking nli systems with sentences that require simple lexical inferences"
    - "hypothesis only baselines in natural language inference"
    - "stress testing neural models of natural language inference with multiply quantified sentences"
    - "stress test evaluation for natural language inference"
    - "evaluating compositionality in sentence embeddings"
    - "still not systematic after all these years on the compositional skills of sequence to sequence recurrent networks"

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)