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: "tracking state changes in procedural text a challenge dataset and models for process paragraph comprehension"
  Abstract: We present a new dataset and models for comprehending paragraphs about processes (e.g., photosynthesis), an important genre of text describing a dynamic world. The new dataset, ProPara, is the first to contain natural (rather than machine-generated) text about a changing world along with a full annotation of entity states (location and existence) during those changes (81k datapoints). The end-task, tracking the location and existence of entities through the text, is challenging because the causal effects of actions are often implicit and need to be inferred. We find that previous models that have worked well on synthetic data achieve only mediocre performance on ProPara, and introduce two new neural models that exploit alternative mechanisms for state prediction, in particular using LSTM input encoding and span prediction. The new models improve accuracy by up to 19%. The dataset and models are available to the community at this http URL

Connected through hyperedge HE-1 (induced by source paper "natural language qa approaches using reasoning with external knowledge"):
  with members:
    - "transformers as soft reasoners over language"
    - "recent advances in natural language inference a survey of benchmarks resources and approaches"
    - "boolq exploring the surprising difficulty of natural yes no questions"
    - "piqa reasoning about physical commonsense in natural language"
    - "language models as knowledge bases"
    - "quoref a reading comprehension dataset with questions requiring coreferential reasoning"
    - "understanding roles and entities datasets and models for natural language inference"
    - "qasc a dataset for question answering via sentence composition"

Connected through hyperedge HE-2 (induced by source paper "annotating and extracting synthesis process of all solid state batteries from scientific literature"):
  with members:
    - "the materials science procedural text corpus annotating materials synthesis procedures with shallow semantic structures"
    - "scibert a pretrained language model for scientific text"
    - "scispacy fast and robust models for biomedical natural language processing"
    - "deep contextualized word representations"
    - "an annotated corpus for machine reading of instructions in wet lab protocols"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "playing by the book an interactive game approach for action graph extraction from text"
    - "automatically extracting action graphs from materials science synthesis procedures"

Connected through hyperedge HE-3 (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"
    - "modeling naive psychology of characters in simple commonsense stories"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "drop a reading comprehension benchmark requiring discrete reasoning over paragraphs"):
  with members:
    - "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"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "duorc towards complex language understanding with paraphrased reading comprehension"
    - "mcscript a novel dataset for assessing machine comprehension using script knowledge"
    - "the web as a knowledge base for answering complex questions"
    - "know what you don t know unanswerable questions for squad"

Connected through hyperedge HE-5 (induced by source paper "procedural reasoning networks for understanding multimodal procedures"):
  with members:
    - "be consistent improving procedural text comprehension using label consistency"
    - "the natural language decathlon multitask learning as question answering"
    - "recipe1m a dataset for learning cross modal embeddings for cooking recipes and food images"
    - "building dynamic knowledge graphs from text using machine reading comprehension"
    - "neural models for reasoning over multiple mentions using coreference"
    - "relational recurrent neural networks"
    - "allennlp a deep semantic natural language processing platform"
    - "deep contextualized word representations"

Connected through hyperedge HE-6 (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"
    - "modeling naive psychology of characters in simple commonsense stories"
    - "annotation artifacts in natural language inference data"
    - "mcscript a novel dataset for assessing machine comprehension using script knowledge"

Connected through hyperedge HE-7 (induced by source paper "qasc a dataset for question answering via sentence composition"):
  with members:
    - "on the capabilities and limitations of reasoning for natural language understanding"
    - "improving question answering with external knowledge"
    - "question answering as global reasoning over semantic abstractions"
    - "drop a reading comprehension benchmark requiring discrete reasoning over paragraphs"
    - "what s missing a knowledge gap guided approach for multi hop question answering"
    - "a systematic classification of knowledge reasoning and context within the arc dataset"
    - "from recognition to cognition visual commonsense reasoning"
    - "can a suit of armor conduct electricity a new dataset for open book question answering"

Connected through hyperedge HE-8 (induced by source paper "effective use of transformer networks for entity tracking"):
  with members:
    - "bert rediscovers the classical nlp pipeline"
    - "spanbert improving pre training by representing and predicting spans"
    - "attention is not explanation"
    - "tracking discrete and continuous entity state for process understanding"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "building dynamic knowledge graphs from text using machine reading comprehension"
    - "deep contextualized word representations"
    - "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)