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: "jump to better conclusions scan both left and right"
  Abstract: Lake and Baroni (2018) recently introduced the SCAN data set, which consists of simple commands paired with action sequences and is intended to test the strong generalization abilities of recurrent sequence-to-sequence models. Their initial experiments suggested that such models may fail because they lack the ability to extract systematic rules. Here, we take a closer look at SCAN and show that it does not always capture the kind of generalization that it was designed for. To mitigate this we propose a complementary dataset, which requires mapping actions back to the original commands, called NACS. We show that models that do well on SCAN do not necessarily do well on NACS, and that NACS exhibits properties more closely aligned with realistic use-cases for sequence-to-sequence models.

Connected through hyperedge HE-1 (induced by source paper "linguistic generalization and compositionality in modern artificial neural networks"):
  with members:
    - "right for the wrong reasons diagnosing syntactic heuristics in natural language inference"
    - "cnns found to jump around more skillfully than rnns compositional generalization in seq2seq convolutional networks"
    - "measuring compositionality in representation learning"
    - "the emergence of number and syntax units in lstm language models"
    - "what do rnn language models learn about filler gap dependencies"
    - "on adversarial examples for character level neural machine translation"
    - "distinct patterns of syntactic agreement errors in recurrent networks and humans"
    - "deep learning a critical appraisal"

Connected through hyperedge HE-2 (induced by source paper "a benchmark for systematic generalization in grounded language understanding"):
  with members:
    - "learning compositional rules via neural program synthesis"
    - "cnns found to jump around more skillfully than rnns compositional generalization in seq2seq convolutional networks"
    - "emergent systematic generalization in a situated agent"
    - "compositional generalization through meta sequence to sequence learning"
    - "good enough compositional data augmentation"
    - "compositional generalization in a deep seq2seq model by separating syntax and semantics"
    - "modeling question asking using neural program generation"
    - "babyai a platform to study the sample efficiency of grounded language learning"

Connected through hyperedge HE-3 (induced by source paper "synonymous generalization in sequence to sequence recurrent networks"):
  with members:
    - "compositional generalization in a deep seq2seq model by separating syntax and semantics"
    - "memorize or generalize searching for a compositional rnn in a haystack"
    - "rearranging the familiar testing compositional generalization in recurrent networks"
    - "generalization without systematicity on the compositional skills of sequence to sequence recurrent networks"
    - "building machines that learn and think like people"
    - "tensorflow a system for large scale machine learning"
    - "tree structured composition in neural networks without tree structured architectures"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-4 (induced by source paper "analyzing and interpreting neural networks for nlp a report on the first blackboxnlp workshop"):
  with members:
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "under the hood using diagnostic classifiers to investigate and improve how language models track agreement information"
    - "indicatements that character language models learn english morpho syntactic units and regularities"
    - "what do rnn language models learn about filler gap dependencies"
    - "rule induction for global explanation of trained models"
    - "understanding convolutional neural networks for text classification"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "lisa explaining recurrent neural network judgments via layer wise semantic accumulation and example to pattern transformation"

Connected through hyperedge HE-5 (induced by source paper "closure assessing systematic generalization of clevr models"):
  with members:
    - "compositional generalization in a deep seq2seq model by separating syntax and semantics"
    - "transparency by design closing the gap between performance and interpretability in visual reasoning"
    - "neural symbolic vqa disentangling reasoning from vision and language understanding"
    - "systematic generalization what is required and can it be learned"
    - "compositional attention networks for machine reasoning"
    - "explainable neural computation via stack neural module networks"
    - "rearranging the familiar testing compositional generalization in recurrent networks"
    - "inferring and executing programs for visual reasoning"

Connected through hyperedge HE-6 (induced by source paper "compositional generalization through meta sequence to sequence learning"):
  with members:
    - "learning to infer program sketches"
    - "the omniglot challenge a 3 year progress report"
    - "human few shot learning of compositional instructions"
    - "good enough compositional data augmentation"
    - "compositional generalization in a deep seq2seq model by separating syntax and semantics"
    - "mutual exclusivity as a challenge for deep neural networks"
    - "systematic generalization what is required and can it be learned"
    - "deep learning a critical appraisal"

Connected through hyperedge HE-7 (induced by source paper "measuring compositional generalization a comprehensive method on realistic data"):
  with members:
    - "measuring compositionality in representation learning"
    - "learning by abstraction the neural state machine"
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "analysing mathematical reasoning abilities of neural models"
    - "the neuro symbolic concept learner interpreting scenes words and sentences from natural supervision"
    - "universal transformers"
    - "compositional generalization in a deep seq2seq model by separating syntax and semantics"
    - "improving text to sql evaluation methodology"

Connected through hyperedge HE-8 (induced by source paper "good enough compositional data augmentation"):
  with members:
    - "eda easy data augmentation techniques for boosting performance on text classification tasks"
    - "human few shot learning of compositional instructions"
    - "learning data manipulation for augmentation and weighting"
    - "improving text to sql evaluation methodology"
    - "deep contextualized word representations"
    - "deep learning a critical appraisal"
    - "on the state of the art of evaluation in neural language models"
    - "attention is all you need"

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)