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: "explain yourself leveraging language models for commonsense reasoning"
  Abstract: Deep learning models perform poorly on tasks that require commonsense reasoning, which often necessitates some form of world-knowledge or reasoning over information not immediately present in the input. We collect human explanations for commonsense reasoning in the form of natural language sequences and highlighted annotations in a new dataset called Common Sense Explanations (CoS-E). We use CoS-E to train language models to automatically generate explanations that can be used during training and inference in a novel Commonsense Auto-Generated Explanation (CAGE) framework. CAGE improves the state-of-the-art by 10% on the challenging CommonsenseQA task. We further study commonsense reasoning in DNNs using both human and auto-generated explanations including transfer to out-of-domain tasks. Empirical results indicate that we can effectively leverage language models for commonsense reasoning.

Connected through hyperedge HE-1 (induced by source paper "learning from explanations with neural execution tree"):
  with members:
    - "hierarchical decision making by generating and following natural language instructions"
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "generalize symbolic knowledge with neural rule engine"
    - "e snli natural language inference with natural language explanations"
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "heterogeneous supervision for relation extraction a representation learning approach"
    - "constructing datasets for multi hop reading comprehension across documents"

Connected through hyperedge HE-2 (induced by source paper "joint reasoning for multi faceted commonsense knowledge"):
  with members:
    - "comet commonsense transformers for automatic knowledge graph construction"
    - "embedding uncertain knowledge graphs"
    - "commonsenseqa a question answering challenge targeting commonsense knowledge"
    - "atomic an atlas of machine commonsense for if then reasoning"
    - "engaging image captioning via personality"
    - "moving beyond the turing test with the allen ai science challenge"
    - "vqa visual question answering"
    - "incremental knowledge base construction using deepdive"

Connected through hyperedge HE-3 (induced by source paper "enteval a holistic evaluation benchmark for entity representations"):
  with members:
    - "hellaswag can a machine really finish your sentence"
    - "joint learning of named entity recognition and entity linking"
    - "barack s wife hillary using knowledge graphs for fact aware language modeling"
    - "evaluation benchmarks and learning criteriafor discourse aware sentence representations"
    - "ernie enhanced representation through knowledge integration"
    - "zero shot entity linking by reading entity descriptions"
    - "socialiqa commonsense reasoning about social interactions"
    - "seeing things from a different angle discovering diverse perspectives about claims"

Connected through hyperedge HE-4 (induced by source paper "eraser a benchmark to evaluate rationalized nlp models"):
  with members:
    - "boolq exploring the surprising difficulty of natural yes no questions"
    - "attention is not not explanation"
    - "on identifiability in transformers"
    - "inferring which medical treatments work from reports of clinical trials"
    - "fine grained sentiment analysis with faithful attention"
    - "on the validity of self attention as explanation in transformer models"
    - "rethinking cooperative rationalization introspective extraction and complement control"
    - "is attention interpretable"

Connected through hyperedge HE-5 (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"
    - "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"
    - "a simple method for commonsense reasoning"

Connected through hyperedge HE-6 (induced by source paper "graph based reasoning over heterogeneous external knowledge for commonsense question answering"):
  with members:
    - "hellaswag can a machine really finish your sentence"
    - "explore propose and assemble an interpretable model for multi hop reading comprehension"
    - "kagnet knowledge aware graph networks for commonsense reasoning"
    - "multi hop reading comprehension across multiple documents by reasoning over heterogeneous graphs"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "align mask and select a simple method for incorporating commonsense knowledge into language representation models"
    - "roberta a robustly optimized bert pretraining approach"
    - "mcscript2 0 a machine comprehension corpus focused on script events and participants"

Connected through hyperedge HE-7 (induced by source paper "kagnet knowledge aware graph networks for commonsense reasoning"):
  with members:
    - "leveraging knowledge bases in lstms for improving machine reading"
    - "does it make sense and why a pilot study for sense making and explanation"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "roberta a robustly optimized bert pretraining approach"
    - "improving question answering by commonsense based pre training"
    - "from recognition to cognition visual commonsense reasoning"
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "learning beyond datasets knowledge graph augmented neural networks for natural language processing"

Connected through hyperedge HE-8 (induced by source paper "how can we know what language models know"):
  with members:
    - "facebook fair s wmt19 news translation task submission"
    - "assessing bert s syntactic abilities"
    - "bert rediscovers the classical nlp pipeline"
    - "bert is not a knowledge base yet factual knowledge vs name based reasoning in unsupervised qa"
    - "barack s wife hillary using knowledge graphs for fact aware language modeling"
    - "mask predict parallel decoding of conditional masked language models"
    - "matching the blanks distributional similarity for relation learning"
    - "latent relation language models"

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)