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: "several experiments on investigating pretraining and knowledge enhanced models for natural language inference"
  Abstract: Natural language inference (NLI) is among the most challenging tasks in natural language understanding. Recent work on unsupervised pretraining that leverages unsupervised signals such as language-model and sentence prediction objectives has shown to be very effective on a wide range of NLP problems. It would still be desirable to further understand how it helps NLI; e.g., if it learns artifacts in data annotation or instead learn true inference knowledge. In addition, external knowledge that does not exist in the limited amount of NLI training data may be added to NLI models in two typical ways, e.g., from human-created resources or an unsupervised pretraining paradigm. We runs several experiments here to investigate whether they help NLI in the same way, and if not,how?

Connected through hyperedge HE-1 (induced by source paper "kernel graph attention network for fact verification"):
  with members:
    - "bert for evidence retrieval and claim verification"
    - "reasoning over semantic level graph for fact checking"
    - "gear graph based evidence aggregating and reasoning for fact verification"
    - "understanding attention and generalization in graph neural networks"
    - "deeper text understanding for ir with contextual neural language modeling"
    - "cedr contextualized embeddings for document ranking"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "what does bert look at an analysis of bert s attention"

Connected through hyperedge HE-2 (induced by source paper "infusing knowledge into the textual entailment task using graph convolutional networks"):
  with members:
    - "multi task deep neural networks for natural language understanding"
    - "sentence embeddings in nli with iterative refinement encoders"
    - "roberta a robustly optimized bert pretraining approach"
    - "i know what you want semantic learning for text comprehension"
    - "annotation artifacts in natural language inference data"
    - "breaking nli systems with sentences that require simple lexical inferences"
    - "hypothesis only baselines in natural language inference"
    - "improving natural language inference using external knowledge in the science questions domain"

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)