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: "sparse and constrained attention for neural machine translation"
  Abstract: In NMT, words are sometimes dropped from the source or generated repeatedly in the translation. We explore novel strategies to address the coverage problem that change only the attention transformation. Our approach allocates fertilities to source words, used to bound the attention each word can receive. We experiment with various sparse and constrained attention transformations and propose a new one, constrained sparsemax, shown to be differentiable and sparse. Empirical evaluation is provided in three languages pairs.

Connected through hyperedge HE-1 (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-2 (induced by source paper "sparse and structured visual attention"):
  with members:
    - "lxmert learning cross modality encoder representations from transformers"
    - "sparse sequence to sequence models"
    - "deep modular co attention networks for visual question answering"
    - "bilinear attention networks"
    - "neural baby talk"
    - "simnet stepwise image topic merging network for generating detailed and comprehensive image captions"
    - "show tell and discriminate image captioning by self retrieval with partially labeled data"
    - "mutan multimodal tucker fusion for visual question answering"

Connected through hyperedge HE-3 (induced by source paper "understanding multi head attention in abstractive summarization"):
  with members:
    - "are sixteen heads really better than one"
    - "analyzing multi head self attention specialized heads do the heavy lifting the rest can be pruned"
    - "adaptively sparse transformers"
    - "analyzing the structure of attention in a transformer language model"
    - "is attention interpretable"
    - "attention is not explanation"
    - "attention interpretability across nlp tasks"
    - "do transformer attention heads provide transparency in abstractive summarization"

Connected through hyperedge HE-4 (induced by source paper "adaptively sparse transformers"):
  with members:
    - "analyzing multi head self attention specialized heads do the heavy lifting the rest can be pruned"
    - "ssn learning sparse switchable normalization via sparsestmax"
    - "bert rediscovers the classical nlp pipeline"
    - "pay less attention with lightweight and dynamic convolutions"
    - "sparse sequence to sequence models"
    - "attention is not explanation"
    - "generating long sequences with sparse transformers"
    - "selective attention for context aware neural machine translation"

Connected through hyperedge HE-5 (induced by source paper "selective attention for context aware neural machine translation"):
  with members:
    - "has machine translation achieved human parity a case for document level evaluation"
    - "improving the transformer translation model with document level context"
    - "context aware neural machine translation learns anaphora resolution"
    - "contextual neural model for translating bilingual multi speaker conversations"
    - "a large scale test set for the evaluation of context aware pronoun translation in neural machine translation"
    - "modeling coherence for neural machine translation with dynamic and topic caches"
    - "neural machine translation with extended context"
    - "learning to remember translation history with a continuous cache"

Connected through hyperedge HE-6 (induced by source paper "the limited multi label projection layer"):
  with members:
    - "linknet relational embedding for scene graph"
    - "smooth loss functions for deep top k classification"
    - "predict and constrain modeling cardinality in deep structured prediction"
    - "neural motifs scene graph parsing with global context"
    - "deep variation structured reinforcement learning for visual relationship and attribute detection"
    - "scene graph generation by iterative message passing"
    - "on support relations and semantic scene graphs"
    - "optnet differentiable optimization as a layer in neural networks"

Connected through hyperedge HE-7 (induced by source paper "sparse sequence to sequence models"):
  with members:
    - "attention is not explanation"
    - "von mises fisher loss for training sequence to sequence models with continuous outputs"
    - "hard non monotonic attention for character level transduction"
    - "attention is all you need"
    - "opennmt open source toolkit for neural machine translation"
    - "classical structured prediction losses for sequence to sequence learning"
    - "massively multilingual neural grapheme to phoneme conversion"
    - "from softmax to sparsemax a sparse model of attention and multi label classification"

Connected through hyperedge HE-8 (induced by source paper "differentiable convex optimization layers"):
  with members:
    - "meta learning with differentiable convex optimization"
    - "the limited multi label projection layer"
    - "deep declarative networks a new hope"
    - "melding the data decisions pipeline decision focused learning for combinatorial optimization"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "neural proximal gradient descent for compressive imaging"
    - "manipulating machine learning poisoning attacks and countermeasures for regression learning"
    - "what game are we playing end to end learning in normal and extensive form games"

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)