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: "a knowledge hunting framework for common sense reasoning"
  Abstract: We introduce an automatic system that achieves state-of-the-art results on the Winograd Schema Challenge (WSC), a common sense reasoning task that requires diverse, complex forms of inference and knowledge. Our method uses a knowledge hunting module to gather text from the web, which serves as evidence for candidate problem resolutions. Given an input problem, our system generates relevant queries to send to a search engine, then extracts and classifies knowledge from the returned results and weighs them to make a resolution. Our approach improves F1 performance on the full WSC by 0.21 over the previous best and represents the first system to exceed 0.5 F1. We further demonstrate that the approach is competitive on the Choice of Plausible Alternatives (COPA) task, which suggests that it is generally applicable.

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"
    - "exploring unsupervised pretraining and sentence structure modelling for winograd schema challenge"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "exploring unsupervised pretraining and sentence structure modelling for winograd schema challenge"):
  with members:
    - "solving hard coreference problems"
    - "universal transformers"
    - "how reasonable are common sense reasoning tasks a case study on the winograd schema challenge and swag"
    - "a simple method for commonsense reasoning"
    - "on the evaluation of common sense reasoning in natural language understanding"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-3 (induced by source paper "aser a large scale eventuality knowledge graph"):
  with members:
    - "joint reasoning for temporal and causal relations"
    - "a surprisingly robust trick for the winograd schema challenge"
    - "sp 10k a large scale evaluation set for selectional preference acquisition"
    - "how reasonable are common sense reasoning tasks a case study on the winograd schema challenge and swag"
    - "a simple method for commonsense reasoning"
    - "on the evaluation of common sense reasoning in natural language understanding"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-4 (induced by source paper "wikicrem a large unsupervised corpus for coreference resolution"):
  with members:
    - "a surprisingly robust trick for the winograd schema challenge"
    - "solving hard coreference problems"
    - "the referential reader a recurrent entity network for anaphora resolution"
    - "mind the gap a balanced corpus of gendered ambiguous pronouns"
    - "gender bias in coreference resolution"
    - "a simple method for commonsense reasoning"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-5 (induced by source paper "a surprisingly robust trick for the winograd schema challenge"):
  with members:
    - "multi task deep neural networks for natural language understanding"
    - "achieving human parity on automatic chinese to english news translation"
    - "how reasonable are common sense reasoning tasks a case study on the winograd schema challenge and swag"
    - "a simple method for commonsense reasoning"
    - "on the evaluation of common sense reasoning in natural language understanding"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "fine tuned language models for text classification"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-6 (induced by source paper "using answer set programming for commonsense reasoning in the winograd schema challenge"):
  with members:
    - "interpreting winograd schemas via the sp theory of intelligence and its realisation in the sp computer model"

Connected through hyperedge HE-7 (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"
    - "exploring unsupervised pretraining and sentence structure modelling for winograd schema challenge"
    - "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"
    - "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)