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: "deriving machine attention from human rationales"
  Abstract: Attention-based models are successful when trained on large amounts of data. In this paper, we demonstrate that even in the low-resource scenario, attention can be learned effectively. To this end, we start with discrete human-annotated rationales and map them into continuous attention. Our central hypothesis is that this mapping is general across domains, and thus can be transferred from resource-rich domains to low-resource ones. Our model jointly learns a domain-invariant representation and induces the desired mapping between rationales and attention. Our empirical results validate this hypothesis and show that our approach delivers significant gains over state-of-the-art baselines, yielding over 15% average error reduction on benchmark datasets.

Connected through hyperedge HE-1 (induced by source paper "text classification with lexicon from preattention mechanism"):
  with members:
    - "attention please a critical review of neural attention models in natural language processing"
    - "no training required exploring random encoders for sentence classification"
    - "lifelong learning for sentiment classification"
    - "topic memory networks for short text classification"
    - "ncrf an open source neural sequence labeling toolkit"
    - "a structured self attentive sentence embedding"
    - "dependency sensitive convolutional neural networks for modeling sentences and documents"
    - "bag of tricks for efficient text classification"

Connected through hyperedge HE-2 (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"
    - "attention is all you need"
    - "from softmax to sparsemax a sparse model of attention and multi label classification"
    - "rationalizing neural predictions"
    - "the concrete distribution a continuous relaxation of discrete random variables"

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 "explanation vs attention a two player game to obtain attention for vqa"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "attention is not explanation"
    - "u cam visual explanation using uncertainty based class activation maps"
    - "bilinear attention networks"
    - "learning to count objects in natural images for visual question answering"
    - "differential attention for visual question answering"
    - "rise randomized input sampling for explanation of black box models"
    - "mutan multimodal tucker fusion for visual question answering"

Connected through hyperedge HE-5 (induced by source paper "evaluating explanation without ground truth in interpretable machine learning"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "an evaluation of the human interpretability of explanation"
    - "learning interpretable models with causal guarantees"
    - "interpretable deep learning in drug discovery"
    - "on attribution of recurrent neural network predictions via additive decomposition"
    - "neural network attributions a causal perspective"
    - "on the in fidelity and sensitivity for explanations"
    - "interpreting neural networks using flip points"

Connected through hyperedge HE-6 (induced by source paper "few shot text classification with distributional signatures"):
  with members:
    - "meta learning with differentiable convex optimization"
    - "huggingface s transformers state of the art natural language processing"
    - "a closer look at few shot classification"
    - "on first order meta learning algorithms"
    - "reptile a scalable metalearning algorithm"
    - "fewrel a large scale supervised few shot relation classification dataset with state of the art evaluation"
    - "multi source domain adaptation with mixture of experts"
    - "how to train your maml"

Connected through hyperedge HE-7 (induced by source paper "attention is not explanation"):
  with members:
    - "explainable prediction of medical codes from clinical text"
    - "interpreting recurrent and attention based neural models a case study on natural language inference"
    - "structured attention networks"
    - "a causal framework for explaining the predictions of black box sequence to sequence models"
    - "attention is all you need"
    - "axiomatic attribution for deep networks"
    - "an interpretable knowledge transfer model for knowledge base completion"
    - "right for the right reasons training differentiable models by constraining their explanations"

Connected through hyperedge HE-8 (induced by source paper "what do deep networks like to read"):
  with members:
    - "on measuring social biases in sentence encoders"
    - "interpretable neural predictions with differentiable binary variables"
    - "winogrande an adversarial winograd schema challenge at scale"
    - "annotation artifacts in natural language inference data"
    - "hypothesis only baselines in natural language inference"
    - "analysis methods in neural language processing a survey"
    - "what do deep networks like to see"
    - "swag a large scale adversarial dataset for grounded commonsense inference"

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)