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: "posing fair generalization tasks for natural language inference"
  Abstract: Deep learning models for semantics are generally evaluated using naturalistic corpora. Adversarial methods, in which models are evaluated on new examples with known semantic properties, have begun to reveal that good performance at these naturalistic tasks can hide serious shortcomings. However, we should insist that these evaluations be fair -that the models are given data sufficient to support the requisite kinds of generalization. In this paper, we define and motivate a formal notion of fairness in this sense. We then apply these ideas to natural language inference by constructing very challenging but provably fair artificial datasets and showing that standard neural models fail to generalize in the required ways; only task-specific models that jointly compose the premise and hypothesis are able to achieve high performance, and even these models do not solve the task perfectly.

Connected through hyperedge HE-1 (induced by source paper "what does my qa model know devising controlled probes using expert knowledge"):
  with members:
    - "right for the wrong reasons diagnosing syntactic heuristics in natural language inference"
    - "overcoming catastrophic forgetting by neuron level plasticity control"
    - "abductive commonsense reasoning"
    - "inoculation by fine tuning a method for analyzing challenge datasets"
    - "from f to a on the n y regents science exams an overview of the aristo project"
    - "diversify your datasets analyzing generalization via controlled variance in adversarial datasets"
    - "qasc a dataset for question answering via sentence composition"
    - "wic the word in context dataset for evaluating context sensitive meaning representations"

Connected through hyperedge HE-2 (induced by source paper "probing natural language inference models through semantic fragments"):
  with members:
    - "can neural networks understand monotonicity reasoning"
    - "right for the wrong reasons diagnosing syntactic heuristics in natural language inference"
    - "monalog a lightweight system for natural language inference based on monotonicity"
    - "assessing bert s syntactic abilities"
    - "inoculation by fine tuning a method for analyzing challenge datasets"
    - "correlating neural and symbolic representations of language"
    - "a logical based corpus for cross lingual evaluation"
    - "investigating bert s knowledge of language five analysis methods with npis"

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)