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: "building an evaluation scale using item response theory"
  Abstract: Evaluation of NLP methods requires testing against a previously vetted gold-standard test set and reporting standard metrics (accuracy/precision/recall/F1). The current assumption is that all items in a given test set are equal with regards to difficulty and discriminating power. We propose Item Response Theory (IRT) from psychometrics as an alternative means for gold-standard test-set generation and NLP system evaluation. IRT is able to describe characteristics of individual items - their difficulty and discriminating power - and can account for these characteristics in its estimation of human intelligence or ability for an NLP task. In this paper, we demonstrate IRT by generating a gold-standard test set for Recognizing Textual Entailment. By collecting a large number of human responses and fitting our IRT model, we show that our IRT model compares NLP systems with the performance in a human population and is able to provide more insight into system performance than standard evaluation metrics. We show that a high accuracy score does not always imply a high IRT score, which depends on the item characteristics and the response pattern.

Connected through hyperedge HE-1 (induced by source paper "sherliic a typed event focused lexical inference benchmark for evaluating natural language inference"):
  with members:
    - "annotation artifacts in natural language inference data"
    - "breaking nli systems with sentences that require simple lexical inferences"
    - "generating natural language adversarial examples"
    - "hearst patterns revisited automatic hypernym detection from large text corpora"
    - "using trusted data to train deep networks on labels corrupted by severe noise"
    - "stress test evaluation for natural language inference"
    - "a broad coverage challenge corpus for sentence understanding through inference"
    - "generating natural adversarial examples"

Connected through hyperedge HE-2 (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-3 (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"
    - "understanding deep learning performance through an examination of test set difficulty a psychometric case study"
    - "neural semantic encoders"

Connected through hyperedge HE-4 (induced by source paper "cift crowd informed fine tuning to improve machine learning ability"):
  with members:
    - "annotation artifacts in natural language inference data"
    - "hypothesis only baselines in natural language inference"
    - "improving machine learning ability with fine tuning"
    - "a broad coverage challenge corpus for sentence understanding through inference"
    - "enhanced lstm for natural language inference"
    - "building machines that learn and think like people"
    - "neural semantic encoders"
    - "theano a python framework for fast computation of mathematical expressions"

Connected through hyperedge HE-5 (induced by source paper "an analysis of machine learning intelligence"):
  with members:
    - "annotation artifacts in natural language inference data"
    - "hypothesis only baselines in natural language inference"
    - "performance impact caused by hidden bias of training data for recognizing textual entailment"
    - "building machines that learn and think like people"
    - "neural semantic encoders"
    - "reasoning about entailment with neural attention"
    - "a large annotated corpus for learning natural language inference"
    - "neural turing machines"

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)