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: "understanding deep learning performance through an examination of test set difficulty a psychometric case study"
  Abstract: Interpreting the performance of deep learning models beyond test set accuracy is challenging. Characteristics of individual data points are often not considered during evaluation, and each data point is treated equally. We examine the impact of a test set question's difficulty to determine if there is a relationship between difficulty and performance. We model difficulty using well-studied psychometric methods on human response patterns. Experiments on Natural Language Inference (NLI) and Sentiment Analysis (SA) show that the likelihood of answering a question correctly is impacted by the question's difficulty. As DNNs are trained with more data, easy examples are learned more quickly than hard examples.

Connected through hyperedge HE-1 (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"
    - "neural models of factuality"
    - "performance impact caused by hidden bias of training data for recognizing textual entailment"
    - "efficient online scalar annotation with bounded support"

Connected through hyperedge HE-2 (induced by source paper "learning latent parameters without human response patterns item response theory with artificial crowds"):
  with members:
    - "bert rediscovers the classical nlp pipeline"
    - "multi task deep neural networks for natural language understanding"
    - "pyro deep universal probabilistic programming"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "how much reading does reading comprehension require a critical investigation of popular benchmarks"
    - "efficient online scalar annotation with bounded support"
    - "building an evaluation scale using item response theory"
    - "neural semantic encoders"

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)