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: "e snli natural language inference with natural language explanations"
  Abstract: In order for machine learning to garner widespread public adoption, models must be able to provide interpretable and robust explanations for their decisions, as well as learn from human-provided explanations at train time. In this work, we extend the Stanford Natural Language Inference dataset with an additional layer of human-annotated natural language explanations of the entailment relations. We further implement models that incorporate these explanations into their training process and output them at test time. We show how our corpus of explanations, which we call e-SNLI, can be used for various goals, such as obtaining full sentence justifications of a model's decisions, improving universal sentence representations and transferring to out-of-domain NLI datasets. Our dataset thus opens up a range of research directions for using natural language explanations, both for improving models and for asserting their trust.

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"
    - "explain yourself leveraging language models for commonsense reasoning"
    - "generalize symbolic knowledge with neural rule engine"
    - "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 "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-3 (induced by source paper "make up your mind adversarial generation of inconsistent natural language explanations"):
  with members:
    - "adversarial attacks on deep learning models in natural language processing a survey"
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "annotation artifacts in natural language inference data"
    - "seq2sick evaluating the robustness of sequence to sequence models with adversarial examples"
    - "textual explanations for self driving vehicles"
    - "adversarially regularising neural nli models to integrate logical background knowledge"
    - "a unified approach to interpreting model predictions"
    - "synthetic and natural noise both break neural machine translation"

Connected through hyperedge HE-4 (induced by source paper "rc qed evaluating natural language derivations in multi hop reading comprehension"):
  with members:
    - "coarse grain fine grain coattention network for multi evidence question answering"
    - "explain yourself leveraging language models for commonsense reasoning"
    - "automated fact checking task formulations methods and future directions"
    - "worldtree a corpus of explanation graphs for elementary science questions supporting multi hop inference"
    - "neural models for reasoning over multiple mentions using coreference"
    - "multi hop inference for sentence level textgraphs how challenging is meaningfully combining information for science question answering"
    - "neural automated essay scoring and coherence modeling for adversarially crafted input"
    - "question answering by reasoning across documents with graph convolutional networks"

Connected through hyperedge HE-5 (induced by source paper "what gets echoed understanding the pointers in explanations of persuasive arguments"):
  with members:
    - "on human predictions with explanations and predictions of machine learning models a case study on deception detection"
    - "rating reliability and bias in news articles does ai assistance help everyone"
    - "explain yourself leveraging language models for commonsense reasoning"
    - "exploring the role of prior beliefs for argument persuasion"
    - "you are no jack kennedy on media selection of highlights from presidential debates"
    - "explanation in artificial intelligence insights from the social sciences"
    - "opennmt open source toolkit for neural machine translation"
    - "winning on the merits the joint effects of content and style on debate outcomes"

Connected through hyperedge HE-6 (induced by source paper "explain yourself leveraging language models for commonsense reasoning"):
  with members:
    - "improving question answering by commonsense based pre training"
    - "a simple method for commonsense reasoning"
    - "the natural language decathlon multitask learning as question answering"
    - "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"
    - "commonsenseqa a question answering challenge targeting commonsense knowledge"
    - "universal language model fine tuning for text classification"

Connected through hyperedge HE-7 (induced by source paper "abductive commonsense reasoning"):
  with members:
    - "hellaswag can a machine really finish your sentence"
    - "bertscore evaluating text generation with bert"
    - "comet commonsense transformers for automatic knowledge graph construction"
    - "winogrande an adversarial winograd schema challenge at scale"
    - "annotation artifacts in natural language inference data"
    - "hypothesis only baselines in natural language inference"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "deep contextualized word representations"

Connected through hyperedge HE-8 (induced by source paper "shaping visual representations with language for few shot classification"):
  with members:
    - "baby steps towards few shot learning with multiple semantics"
    - "using natural language for reward shaping in reinforcement learning"
    - "explain yourself leveraging language models for commonsense reasoning"
    - "a closer look at few shot classification"
    - "adaptive cross modal few shot learning"
    - "grounding visual explanations"
    - "learning to understand goal specifications by modelling reward"
    - "learning compositional representations for few shot recognition"

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)