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: "learning attitudes and attributes from multi aspect reviews"
  Abstract: The majority of online reviews consist of plain-text feedback together with a single numeric score. However, there are multiple dimensions to products and opinions, and understanding the `aspects' that contribute to users' ratings may help us to better understand their individual preferences. For example, a user's impression of an audiobook presumably depends on aspects such as the story and the narrator, and knowing their opinions on these aspects may help us to recommend better products. In this paper, we build models for rating systems in which such dimensions are explicit, in the sense that users leave separate ratings for each aspect of a product. By introducing new corpora consisting of five million reviews, rated with between three and six aspects, we evaluate our models on three prediction tasks: First, we use our model to uncover which parts of a review discuss which of the rated aspects. Second, we use our model to summarize reviews, which for us means finding the sentences that best explain a user's rating. Finally, since aspect ratings are optional in many of the datasets we consider, we use our model to recover those ratings that are missing from a user's evaluation. Our model matches state-of-the-art approaches on existing small-scale datasets, while scaling to the real-world datasets we introduce. Moreover, our model is able to `disentangle' content and sentiment words: we automatically learn content words that are indicative of a particular aspect as well as the aspect-specific sentiment words that are indicative of a particular rating.

Connected through hyperedge HE-1 (induced by source paper "multi dimensional explanation of ratings from reviews"):
  with members:
    - "is attention interpretable"
    - "attention is not explanation"
    - "methods for interpreting and understanding deep neural networks"
    - "deep contextualized word representations"
    - "deriving machine attention from human rationales"
    - "attention is all you need"
    - "from softmax to sparsemax a sparse model of attention and multi label classification"
    - "rationalizing neural predictions"

Connected through hyperedge HE-2 (induced by source paper "hotelrec a novel very large scale hotel recommendation dataset"):
  with members:
    - "multi dimensional explanation of reviews"
    - "neural graph collaborative filtering"
    - "aspect aware latent factor model rating prediction with ratings and reviews"
    - "neural collaborative filtering"
    - "joint deep modeling of users and items using reviews for recommendation"
    - "ups and downs modeling the visual evolution of fashion trends with one class collaborative filtering"
    - "understanding neural networks through representation erasure"
    - "image based recommendations on styles and substitutes"

Connected through hyperedge HE-3 (induced by source paper "learning credible deep neural networks with rationale regularization"):
  with members:
    - "on attribution of recurrent neural network predictions via additive decomposition"
    - "evaluating explanation without ground truth in interpretable machine learning"
    - "towards explanation of dnn based prediction with guided feature inversion"
    - "investigating backtranslation in neural machine translation"
    - "women also snowboard overcoming bias in captioning models"
    - "annotation artifacts in natural language inference data"
    - "knowledgeable reader enhancing cloze style reading comprehension with external commonsense knowledge"
    - "techniques for interpretable machine learning"

Connected through hyperedge HE-4 (induced by source paper "practical compositional fairness understanding fairness in multi task ml systems"):
  with members:
    - "fairness aware ranking in search recommendation systems with application to linkedin talent search"
    - "putting fairness principles into practice challenges metrics and improvements"
    - "nuanced metrics for measuring unintended bias with real data for text classification"
    - "fairness in recommendation ranking through pairwise comparisons"
    - "the fairness of risk scores beyond classification bipartite ranking and the xauc metric"
    - "pairwise fairness for ranking and regression"
    - "training well generalizing classifiers for fairness metrics and other data dependent constraints"
    - "multiaccuracy black box post processing for fairness in classification"

Connected through hyperedge HE-5 (induced by source paper "a game theoretic approach to class wise selective rationalization"):
  with members:
    - "functional transparency for structured data a game theoretic approach"
    - "towards robust locally linear deep networks"
    - "rethinking cooperative rationalization introspective extraction and complement control"
    - "towards robust interpretability with self explaining neural networks"
    - "l shapley and c shapley efficient model interpretation for structured data"
    - "learning to explain an information theoretic perspective on model interpretation"
    - "deriving machine attention from human rationales"
    - "inferring and executing programs for visual reasoning"

Connected through hyperedge HE-6 (induced by source paper "can i trust the explainer verifying post hoc explanatory methods"):
  with members:
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "annotation artifacts in natural language inference data"
    - "evaluating neural network explanation methods using hybrid documents and morphological prediction"
    - "breaking nli systems with sentences that require simple lexical inferences"
    - "model agnostic supervised local explanations"
    - "what made you do this understanding black box decisions with sufficient input subsets"
    - "textual explanations for self driving vehicles"
    - "e snli natural language inference with natural language explanations"

Connected through hyperedge HE-7 (induced by source paper "cross domain aspect category transfer and detection via traceable heterogeneous graph representation learning"):
  with members:
    - "mathematics content understanding for cyberlearning via formula evolution map"
    - "cross language citation recommendation via hierarchical representation learning on heterogeneous graph"
    - "attention is all you need"
    - "recent trends in deep learning based natural language processing"
    - "aspect level sentiment classification with deep memory network"
    - "node2vec scalable feature learning for networks"
    - "insight 1 at semeval 2016 task 5 deep learning for multilingual aspect based sentiment analysis"
    - "semi supervised classification with graph convolutional networks"

Connected through hyperedge HE-8 (induced by source paper "educe explaining model decisions through unsupervised concepts extraction"):
  with members:
    - "xoc explainable observer classifier for explainable binary decisions"
    - "interpretable neural predictions with differentiable binary variables"
    - "counterfactual visual explanations"
    - "learning word vectors for 157 languages"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a causal framework for explaining the predictions of black box sequence to sequence models"
    - "axiomatic attribution for deep networks"
    - "rationalizing neural predictions"

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)