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: "modeling naive psychology of characters in simple commonsense stories"
  Abstract: Understanding a narrative requires reading between the lines and reasoning about the unspoken but obvious implications about events and people's mental states - a capability that is trivial for humans but remarkably hard for machines. To facilitate research addressing this challenge, we introduce a new annotation framework to explain naive psychology of story characters as fully-specified chains of mental states with respect to motivations and emotional reactions. Our work presents a new large-scale dataset with rich low-level annotations and establishes baseline performance on several new tasks, suggesting avenues for future research.

Connected through hyperedge HE-1 (induced by source paper "a knowledge enhanced pretraining model for commonsense story generation"):
  with members:
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "fine tuning pre trained transformer language models to distantly supervised relation extraction"
    - "long and diverse text generation with planning based hierarchical variational model"
    - "the curious case of neural text degeneration"
    - "pretraining methods for dialog context representation learning"
    - "ernie enhanced representation through knowledge integration"
    - "do massively pretrained language models make better storytellers"
    - "mass masked sequence to sequence pre training for language generation"

Connected through hyperedge HE-2 (induced by source paper "commonsense reasoning for natural language understanding a survey of benchmarks resources and approaches"):
  with members:
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "multi task deep neural networks for natural language understanding"
    - "explicit bias discovery in visual question answering models"
    - "from recognition to cognition visual commonsense reasoning"
    - "annotation artifacts in natural language inference data"
    - "can a suit of armor conduct electricity a new dataset for open book question answering"
    - "gender bias in coreference resolution"
    - "a simple method for commonsense reasoning"

Connected through hyperedge HE-3 (induced by source paper "cosmos qa machine reading comprehension with contextual commonsense reasoning"):
  with members:
    - "bertscore evaluating text generation with bert"
    - "socialiqa commonsense reasoning about social interactions"
    - "dream a challenge dataset and models for dialogue based reading comprehension"
    - "dual co matching network for multi choice reading comprehension"
    - "yuanfudao at semeval 2018 task 11 three way attention and relational knowledge for commonsense machine comprehension"
    - "annotation artifacts in natural language inference data"
    - "mcscript a novel dataset for assessing machine comprehension using script knowledge"
    - "tracking state changes in procedural text a challenge dataset and models for process paragraph comprehension"

Connected through hyperedge HE-4 (induced by source paper "dynamic knowledge graph construction for zero shot commonsense question answering"):
  with members:
    - "coarse grain fine grain coattention network for multi evidence question answering"
    - "explore propose and assemble an interpretable model for multi hop reading comprehension"
    - "using local knowledge graph construction to scale seq2seq models to multi document inputs"
    - "comet commonsense transformers for automatic knowledge graph construction"
    - "self assembling modular networks for interpretable multi hop reasoning"
    - "kagnet knowledge aware graph networks for commonsense reasoning"
    - "ranking and selecting multi hop knowledge paths to better predict human needs"
    - "learning to write with cooperative discriminators"

Connected through hyperedge HE-5 (induced by source paper "unsupervised deep structured semantic models for commonsense reasoning"):
  with members:
    - "solving hard coreference problems"
    - "a simple method for commonsense reasoning"
    - "deep contextualized word representations"
    - "neural approaches to conversational ai"
    - "event2mind commonsense inference on events intents and reactions"
    - "record bridging the gap between human and machine commonsense reading comprehension"
    - "swag a large scale adversarial dataset for grounded commonsense inference"
    - "a broad coverage challenge corpus for sentence understanding through inference"

Connected through hyperedge HE-6 (induced by source paper "ranking and selecting multi hop knowledge paths to better predict human needs"):
  with members:
    - "deep learning for sentiment analysis a survey"
    - "knowledgeable reader enhancing cloze style reading comprehension with external commonsense knowledge"
    - "dynamic integration of background knowledge in neural nlu systems"
    - "deep contextualized word representations"
    - "zero shot sequence labeling transferring knowledge from sentences to tokens"
    - "commonsense for generative multi hop question answering tasks"
    - "reflections on sentiment opinion analysis"
    - "simulating action dynamics with neural process networks"

Connected through hyperedge HE-7 (induced by source paper "asking the right question inferring advice seeking intentions from personal narratives"):
  with members:
    - "story ending generation with incremental encoding and commonsense knowledge"
    - "learning to ask good questions ranking clarification questions using neural expected value of perfect information"
    - "coqa a conversational question answering challenge"
    - "quac question answering in context"
    - "learning to ask neural question generation for reading comprehension"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "text summarization techniques a brief survey"
    - "the narrativeqa reading comprehension challenge"

Connected through hyperedge HE-8 (induced by source paper "towards empathetic open domain conversation models a new benchmark and dataset"):
  with members:
    - "the second conversational intelligence challenge convai2"
    - "multi task deep neural networks for natural language understanding"
    - "learning semantic textual similarity from conversations"
    - "plusemo2vec at semeval 2018 task 1 exploiting emotion knowledge from emoji and hashtags"
    - "training millions of personalized dialogue agents"
    - "emo2vec learning generalized emotion representation by multi task training"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "polite dialogue generation without parallel data"

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)