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: "a compare propagate architecture with alignment factorization for natural language inference"
  Abstract: This paper presents a new deep learning architecture for Natural Language Inference (NLI). Firstly, we introduce a new compare-propagate architecture where alignments pairs are compared and then propagated to upper layers for enhanced representation learning. Secondly, we adopt novel factorization layers for efficient compression of alignment vectors into scalar valued features, which are then be used to augment the base word representations. The design of our approach is aimed to be conceptually simple, compact and yet powerful. We conduct experiments on three popular benchmarks, SNLI, MultiNLI and SciTail, achieving state-of-the-art performance on all. A lightweight parameterization of our model enjoys a $\approx 300\%$ reduction in parameter size compared to the ESIM and DIIN, while maintaining competitive performance. Visual analysis shows that our propagated features are highly interpretable, opening new avenues to explainability in neural NLI models.

Connected through hyperedge HE-1 (induced by source paper "discourse marker augmented network with reinforcement learning for natural language inference"):
  with members:
    - "natural language inference over interaction space"
    - "memen multi layer embedding with memory networks for machine comprehension"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "discourse based objectives for fast unsupervised sentence representation learning"
    - "dissent sentence representation learning from explicit discourse relations"
    - "learned in translation contextualized word vectors"
    - "shortcut stacked sentence encoders for multi domain inference"
    - "compare compress and propagate enhancing neural architectures with alignment factorization for natural language inference"

Connected through hyperedge HE-2 (induced by source paper "several experiments on investigating pretraining and knowledge enhanced models for natural language inference"):
  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"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "enhancing sentence embedding with generalized pooling"
    - "dynamic self attention computing attention over words dynamically for sentence embedding"
    - "what if we simply swap the two text fragments a straightforward yet effective way to test the robustness of methods to confounding signals in nature language inference tasks"

Connected through hyperedge HE-3 (induced by source paper "combining similarity features and deep representation learning for stance detection in the context of checking fake news"):
  with members:
    - "discourse marker augmented network with reinforcement learning for natural language inference"
    - "universal transformers"
    - "reinforced self attention network a hybrid of hard and soft attention for sequence modeling"
    - "automatic stance detection using end to end memory networks"
    - "co stack residual affinity networks with multi level attention refinement for matching text sequences"
    - "fakenewsnet a data repository with news content social context and dynamic information for studying fake news on social media"
    - "declare debunking fake news and false claims using evidence aware deep learning"
    - "debunking fake news one feature at a time"

Connected through hyperedge HE-4 (induced by source paper "densely connected attention propagation for reading comprehension"):
  with members:
    - "a question focused multi factor attention network for question answering"
    - "dynamic integration of background knowledge in neural nlu systems"
    - "densely connected bidirectional lstm with applications to sentence classification"
    - "attention guided answer distillation for machine reading comprehension"
    - "deep contextualized word representations"
    - "multi range reasoning for machine comprehension"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-5 (induced by source paper "self attentive neural collaborative filtering"):
  with members:
    - "deep learning based recommender system a survey and new perspectives"
    - "neurec on nonlinear transformation for personalized ranking"
    - "latent relational metric learning via memory based attention for collaborative ranking"
    - "outer product based neural collaborative filtering"
    - "reasoning with sarcasm by reading in between"
    - "multi pointer co attention networks for recommendation"
    - "couplenet paying attention to couples with coupled attention for relationship recommendation"
    - "neural brane neural bayesian personalized ranking for attributed network embedding"

Connected through hyperedge HE-6 (induced by source paper "multi turn inference matching network for natural language inference"):
  with members:
    - "dr bilstm dependent reading bidirectional lstm for natural language inference"
    - "natural language inference over interaction space"
    - "natural language inference from multiple premises"
    - "learned in translation contextualized word vectors"
    - "shortcut stacked sentence encoders for multi domain inference"
    - "compare compress and propagate enhancing neural architectures with alignment factorization for natural language inference"
    - "bilateral multi perspective matching for natural language sentences"
    - "recurrent neural network based sentence encoder with gated attention for natural language inference"

Connected through hyperedge HE-7 (induced by source paper "enhancing sentence embedding with generalized pooling"):
  with members:
    - "fine grained attention mechanism for neural machine translation"
    - "reinforced self attention network a hybrid of hard and soft attention for sequence modeling"
    - "neural natural language inference models enhanced with external knowledge"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "attention is all you need"
    - "shortcut stacked sentence encoders for multi domain inference"
    - "a broad coverage challenge corpus for sentence understanding through inference"
    - "exploring question understanding and adaptation in neural network based question answering"

Connected through hyperedge HE-8 (induced by source paper "regmapr text matching made easy"):
  with members:
    - "learning semantic textual similarity from conversations"
    - "reinforced self attention network a hybrid of hard and soft attention for sequence modeling"
    - "enhancing sentence embedding with generalized pooling"
    - "natural language inference over interaction space"
    - "on the state of the art of evaluation in neural language models"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "shortcut stacked sentence encoders for multi domain inference"
    - "semeval 2017 task 1 semantic textual similarity multilingual and crosslingual focused evaluation"

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)