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: "adversarial evaluation for models of natural language"
  Abstract: We now have a rich and growing set of modeling tools and algorithms for inducing linguistic structure from text that is less than fully annotated. In this paper, we discuss some of the weaknesses of our current methodology. We present a new abstract framework for evaluating natural language processing (NLP) models in general and unsupervised NLP models in particular. The central idea is to make explicit certain adversarial roles among researchers, so that the different roles in an evaluation are more clearly defined and performers of all roles are offered ways to make measurable contributions to the larger goal. Adopting this approach may help to characterize model successes and failures by encouraging earlier consideration of error analysis. The framework can be instantiated in a variety of ways, simulating some familiar intrinsic and extrinsic evaluations as well as some new evaluations. This paper presents a new approach to evaluating computational models of natural language, based on adversarial roles performed by different researchers or their models. We begin inx2 with a brief review of current evaluation strategies in NLP. We then turn to coupled adversarial evaluations inspired by perplexity (x3) and the traditional roles of linguists (x4). The two-performer setup is formalized inx5. We then consider the origins of the data and growing awareness of the importance of context on language use (x6) and provide a three-performer setup, in which a third performer manages data selection (x7). We close with a few open questions (x8).

Connected through hyperedge HE-1 (induced by source paper "stress test evaluation of transformer based models in natural language understanding tasks"):
  with members:
    - "huggingface s transformers state of the art natural language processing"
    - "universal transformers"
    - "a multiscale visualization of attention in the transformer model"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "probing neural network comprehension of natural language arguments"
    - "roberta a robustly optimized bert pretraining approach"
    - "is bert really robust a strong baseline for natural language attack on text classification and entailment"
    - "on adversarial examples for character level neural machine translation"

Connected through hyperedge HE-2 (induced by source paper "stress test evaluation for natural language inference"):
  with members:
    - "annotation artifacts in natural language inference data"
    - "hypothesis only baselines in natural language inference"
    - "dr bilstm dependent reading bidirectional lstm for natural language inference"
    - "a challenge set approach to evaluating machine translation"
    - "the repeval 2017 shared task multi genre natural language inference with sentence representations"
    - "evaluating discourse phenomena in neural machine translation"
    - "shortcut stacked sentence encoders for multi domain inference"
    - "synthetic and natural noise both break neural machine translation"

Connected through hyperedge HE-3 (induced by source paper "adversarial examples for evaluating reading comprehension systems"):
  with members:
    - "structural embedding of syntactic trees for machine comprehension"
    - "end to end answer chunk extraction and ranking for reading comprehension"
    - "learning recurrent span representations for extractive question answering"
    - "making neural qa as simple as possible but not simpler"
    - "adversarial learning for neural dialogue generation"
    - "reasonet learning to stop reading in machine comprehension"
    - "ruminating reader reasoning with gated multi hop attention"
    - "simple black box adversarial perturbations for deep 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)