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: "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
  Abstract: In response to the continuing research interest in computational semantic analysis, we have proposed a new task for SemEval-2010: multi-way classification of mutually exclusive semantic relations between pairs of nominals. The task is designed to compare different approaches to the problem and to provide a standard testbed for future research. In this paper, we define the task, describe the creation of the datasets, and discuss the results of the participating 28 systems submitted by 10 teams.

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"
    - "explain yourself leveraging language models for commonsense reasoning"
    - "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 "social media attributions in the context of water crisis"):
  with members:
    - "who blames whom in a crisis detecting blame ties from news articles using neural networks"
    - "voice for the voiceless active sampling to detect comments supporting the rohingyas"
    - "kashmir a computational analysis of the voice of peace"
    - "huggingface s transformers state of the art natural language processing"
    - "analyzing polarization in social media method and application to tweets on 21 mass shootings"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attribution of responsibility and blame regarding a man made disaster flintwatercrisis"

Connected through hyperedge HE-3 (induced by source paper "analyzing and interpreting neural networks for nlp a report on the first blackboxnlp workshop"):
  with members:
    - "under the hood using diagnostic classifiers to investigate and improve how language models track agreement information"
    - "indicatements that character language models learn english morpho syntactic units and regularities"
    - "what do rnn language models learn about filler gap dependencies"
    - "rule induction for global explanation of trained models"
    - "understanding convolutional neural networks for text classification"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "lisa explaining recurrent neural network judgments via layer wise semantic accumulation and example to pattern transformation"
    - "context free transductions with neural stacks"

Connected through hyperedge HE-4 (induced by source paper "knowledge enhanced contextual word representations"):
  with members:
    - "barack s wife hillary using knowledge graphs for fact aware language modeling"
    - "leveraging knowledge bases in lstms for improving machine reading"
    - "wic the word in context dataset for evaluating context sensitive meaning representations"
    - "matching the blanks distributional similarity for relation learning"
    - "ernie enhanced language representation with informative entities"
    - "tucker tensor factorization for knowledge graph completion"
    - "simple bert models for relation extraction and semantic role labeling"
    - "superglue a stickier benchmark for general purpose language understanding systems"

Connected through hyperedge HE-5 (induced by source paper "learning to few shot learn across diverse natural language classification tasks"):
  with members:
    - "parameter efficient transfer learning for nlp"
    - "bert rediscovers the classical nlp pipeline"
    - "meta learning for low resource natural language generation in task oriented dialogue systems"
    - "learning and evaluating general linguistic intelligence"
    - "to tune or not to tune adapting pretrained representations to diverse tasks"
    - "multi task deep neural networks for natural language understanding"
    - "meta dataset a dataset of datasets for learning to learn from few examples"
    - "xlnet generalized autoregressive pretraining for language understanding"

Connected through hyperedge HE-6 (induced by source paper "generalizing natural language analysis through span relation representations"):
  with members:
    - "enriching pre trained language model with entity information for relation classification"
    - "bert rediscovers the classical nlp pipeline"
    - "spanbert improving pre training by representing and predicting spans"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "multi task deep neural networks for natural language understanding"
    - "simple bert models for relation extraction and semantic role labeling"
    - "bert post training for review reading comprehension and aspect based sentiment analysis"
    - "span based open information extraction"

Connected through hyperedge HE-7 (induced by source paper "what do you learn from context probing for sentence structure in contextualized word representations"):
  with members:
    - "neural davidsonian semantic proto role labeling"
    - "assessing composition in sentence vector representations"
    - "on the evaluation of semantic phenomena in neural machine translation using natural language inference"
    - "collecting diverse natural language inference problems for sentence representation evaluation"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "allennlp a deep semantic natural language processing platform"
    - "jointly predicting predicates and arguments in neural semantic role labeling"
    - "deep contextualized word representations"

Connected through hyperedge HE-8 (induced by source paper "reflex flexible framework for relation extraction in multiple domains"):
  with members:
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "publicly available clinical bert embeddings"
    - "scispacy fast and robust models for biomedical natural language processing"
    - "classifying medical relations in clinical text via convolutional neural networks"
    - "eth ds3lab at semeval 2018 task 7 effectively combining recurrent and convolutional neural networks for relation classification and extraction"
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "deep contextualized word representations"
    - "a hybrid deep learning approach for medical relation extraction"

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)