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: "detecting and explaining causes from text for a time series event"
  Abstract: Explaining underlying causes or effects about events is a challenging but valuable task. We define a novel problem of generating explanations of a time series event by (1) searching cause and effect relationships of the time series with textual data and (2) constructing a connecting chain between them to generate an explanation. To detect causal features from text, we propose a novel method based on the Granger causality of time series between features extracted from text such as N-grams, topics, sentiments, and their composition. The generation of the sequence of causal entities requires a commonsense causative knowledge base with efficient reasoning. To ensure good interpretability and appropriate lexical usage we combine symbolic and neural representations, using a neural reasoning algorithm trained on commonsense causal tuples to predict the next cause step. Our quantitative and human analysis show empirical evidence that our method successfully extracts meaningful causality relationships between time series with textual features and generates appropriate explanation between them.

Connected through hyperedge HE-1 (induced by source paper "xslue a benchmark and analysis platform for cross style language understanding and evaluation"):
  with members:
    - "deep contextualized word representations for detecting sarcasm and irony"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "dear sir or madam may i introduce the yafc corpus corpus benchmarks and metrics for formality style transfer"
    - "automated hate speech detection and the problem of offensive language"
    - "sarcasm sign interpreting sarcasm with sentiment based monolingual machine translation"
    - "controlling linguistic style aspects in neural language generation"
    - "dailydialog a manually labelled multi turn dialogue dataset"

Connected through hyperedge HE-2 (induced by source paper "linguistic versus latent relations for modeling coherent flow in paragraphs"):
  with members:
    - "describing a knowledge base"
    - "a dataset of peer reviews peerread collection insights and nlp applications"
    - "adventure adversarial training for textual entailment with knowledge guided examples"
    - "survey of the state of the art in natural language generation core tasks applications and evaluation"
    - "variational neural discourse relation recognizer"
    - "how not to evaluate your dialogue system an empirical study of unsupervised evaluation metrics for dialogue response generation"
    - "hierarchical multiscale recurrent neural networks"
    - "a hierarchical latent variable encoder decoder model for generating dialogues"

Connected through hyperedge HE-3 (induced by source paper "adventure adversarial training for textual entailment with knowledge guided examples"):
  with members:
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "annotation artifacts in natural language inference data"
    - "breaking nli systems with sentences that require simple lexical inferences"
    - "deep contextualized word representations"
    - "catgan coupled adversarial transfer for domain generation"
    - "natural language inference over interaction space"
    - "triple generative adversarial nets"
    - "learned in translation contextualized word vectors"

Connected through hyperedge HE-4 (induced by source paper "event2mind commonsense inference on events intents and reactions"):
  with members:
    - "analyzing gender stereotyping in bollywood movies"
    - "learning to paraphrase for question answering"
    - "a question answering approach to emotion cause extraction"
    - "ordinal common sense inference"
    - "generating sentences from a continuous space"
    - "a large annotated corpus for learning natural language inference"
    - "connotation frames a data driven investigation"
    - "on the properties of neural machine translation encoder decoder approaches"

Connected through hyperedge HE-5 (induced by source paper "bridging knowledge gaps in neural entailment via symbolic models"):
  with members:
    - "annotation artifacts in natural language inference data"
    - "adventure adversarial training for textual entailment with knowledge guided examples"
    - "compare compress and propagate enhancing neural architectures with alignment factorization for natural language inference"
    - "answering complex questions using open information extraction"
    - "question answering via integer programming over semi structured knowledge"
    - "neural symbolic machines learning semantic parsers on freebase with weak supervision"
    - "a decomposable attention model for natural language inference"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-6 (induced by source paper "framing and agenda setting in russian news a computational analysis of intricate political strategies"):
  with members:
    - "efficient estimation of word representations in vector space"

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)