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: "refining raw sentence representations for textual entailment recognition via attention"
  Abstract: In this paper we present the model used by the team Rivercorners for the 2017 RepEval shared task. First, our model separately encodes a pair of sentences into variable-length representations by using a bidirectional LSTM. Later, it creates fixed-length raw representations by means of simple aggregation functions, which are then refined using an attention mechanism. Finally it combines the refined representations of both sentences into a single vector to be used for classification. With this model we obtained test accuracies of 72.057% and 72.055% in the matched and mismatched evaluation tracks respectively, outperforming the LSTM baseline, and obtaining performances similar to a model that relies on shared information between sentences (ESIM). When using an ensemble both accuracies increased to 72.247% and 72.827% respectively.

Connected through hyperedge HE-1 (induced by source paper "temporal and aspectual entailment"):
  with members:
    - "collecting diverse natural language inference problems for sentence representation evaluation"
    - "allennlp a deep semantic natural language processing platform"
    - "deep contextualized word representations"
    - "using j k fold cross validation to reduce variance when tuning nlp models"
    - "one representation per word does it make sense for composition"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "attention is all you need"
    - "advances in pre training distributed word representations"

Connected through hyperedge HE-2 (induced by source paper "sentence embeddings in nli with iterative refinement encoders"):
  with members:
    - "annotation artifacts in natural language inference data"
    - "breaking nli systems with sentences that require simple lexical inferences"
    - "hypothesis only baselines in natural language inference"
    - "enhancing sentence embedding with generalized pooling"
    - "dynamic self attention computing attention over words dynamically for sentence embedding"
    - "what you can cram into a single vector probing sentence embeddings for linguistic properties"
    - "testing the generalization power of neural network models across nli benchmarks"
    - "senteval an evaluation toolkit for universal sentence representations"

Connected through hyperedge HE-3 (induced by source paper "equate a benchmark evaluation framework for quantitative reasoning in natural language inference"):
  with members:
    - "annotation artifacts in natural language inference data"
    - "breaking nli systems with sentences that require simple lexical inferences"
    - "what knowledge is needed to solve the rte5 textual entailment challenge"
    - "hypothesis only baselines in natural language inference"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "stress test evaluation for natural language inference"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "attention is all you need"

Connected through hyperedge HE-4 (induced by source paper "natural language inference with hierarchical bilstm max pooling architecture"):
  with members:
    - "annotation artifacts in natural language inference data"
    - "reinforced self attention network a hybrid of hard and soft attention for sequence modeling"
    - "breaking nli systems with sentences that require simple lexical inferences"
    - "enhancing sentence embedding with generalized pooling"
    - "dynamic self attention computing attention over words dynamically for sentence embedding"
    - "what you can cram into a single vector probing sentence embeddings for linguistic properties"
    - "testing the generalization power of neural network models across nli benchmarks"
    - "senteval an evaluation toolkit for universal sentence representations"

Connected through hyperedge HE-5 (induced by source paper "stress test evaluation for natural language inference"):
  with members:
    - "annotation artifacts in natural language inference data"
    - "hypothesis only baselines in natural language inference"
    - "dr bilstm dependent reading bidirectional lstm for natural language inference"
    - "a challenge set approach to evaluating machine translation"
    - "the repeval 2017 shared task multi genre natural language inference with sentence representations"
    - "evaluating discourse phenomena in neural machine translation"
    - "shortcut stacked sentence encoders for multi domain inference"
    - "synthetic and natural noise both break neural machine translation"

Connected through hyperedge HE-6 (induced by source paper "distance based self attention network for natural language inference"):
  with members:
    - "character level intra attention network for natural language inference"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "attention is all you need"
    - "the repeval 2017 shared task multi genre natural language inference with sentence representations"
    - "shortcut stacked sentence encoders for multi domain inference"
    - "dynamic routing between capsules"
    - "a broad coverage challenge corpus for sentence understanding through inference"
    - "recurrent neural network based sentence encoder with gated attention for natural language inference"

Connected through hyperedge HE-7 (induced by source paper "natural language inference over interaction space"):
  with members:
    - "shortcut stacked sentence encoders for multi domain inference"
    - "a broad coverage challenge corpus for sentence understanding through inference"
    - "bilateral multi perspective matching for natural language sentences"
    - "reading wikipedia to answer open domain questions"
    - "recurrent neural network based sentence encoder with gated attention for natural language inference"
    - "enhanced lstm for natural language inference"
    - "coherent dialogue with attention based language models"
    - "learning natural language inference using bidirectional lstm model and inner attention"

Connected through hyperedge HE-8 (induced by source paper "the repeval 2017 shared task multi genre natural language inference with sentence representations"):
  with members:
    - "character level intra attention network for natural language inference"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "shortcut stacked sentence encoders for multi domain inference"
    - "a broad coverage challenge corpus for sentence understanding through inference"
    - "recurrent neural network based sentence encoder with gated attention for natural language inference"
    - "enhanced lstm for natural language inference"
    - "character aware neural language models"
    - "learning natural language inference with lstm"

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)