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: "exploring unsupervised pretraining and sentence structure modelling for winograd schema challenge"
  Abstract: Winograd Schema Challenge (WSC) was proposed as an AI-hard problem in testing computers' intelligence on common sense representation and reasoning. This paper presents the new state-of-theart on WSC, achieving an accuracy of 71.1%. We demonstrate that the leading performance benefits from jointly modelling sentence structures, utilizing knowledge learned from cutting-edge pretraining models, and performing fine-tuning. We conduct detailed analyses, showing that fine-tuning is critical for achieving the performance, but it helps more on the simpler associative problems. Modelling sentence dependency structures, however, consistently helps on the harder non-associative subset of WSC. Analysis also shows that larger fine-tuning datasets yield better performances, suggesting the potential benefit of future work on annotating more Winograd schema sentences.

Connected through hyperedge HE-1 (induced by source paper "align mask and select a simple method for incorporating commonsense knowledge into language representation models"):
  with members:
    - "a surprisingly robust trick for the winograd schema challenge"
    - "ernie enhanced language representation with informative entities"
    - "explain yourself leveraging language models for commonsense reasoning"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "roberta a robustly optimized bert pretraining approach"
    - "improving question answering by commonsense based pre training"
    - "how reasonable are common sense reasoning tasks a case study on the winograd schema challenge and swag"
    - "a simple method for commonsense reasoning"

Connected through hyperedge HE-2 (induced by source paper "can a gorilla ride a camel learning semantic plausibility from text"):
  with members:
    - "universal dependency parsing from scratch"
    - "solving hard coreference problems"
    - "sp 10k a large scale evaluation set for selectional preference acquisition"
    - "do neural language representations learn physical commonsense"
    - "probing neural network comprehension of natural language arguments"
    - "winogrande an adversarial winograd schema challenge at scale"
    - "annotation artifacts in natural language inference data"
    - "hypothesis only baselines in natural language inference"

Connected through hyperedge HE-3 (induced by source paper "how reasonable are common sense reasoning tasks a case study on the winograd schema challenge and swag"):
  with members:
    - "socialiqa commonsense reasoning about social interactions"
    - "annotation artifacts in natural language inference data"
    - "a simple method for commonsense reasoning"
    - "the natural language decathlon multitask learning as question answering"
    - "breaking nli systems with sentences that require simple lexical inferences"
    - "a knowledge hunting framework for common sense reasoning"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "bert pre training of deep bidirectional transformers for language understanding"

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)