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: "compositional generalization in a deep seq2seq model by separating syntax and semantics"
  Abstract: Standard methods in deep learning for natural language processing fail to capture the compositional structure of human language that allows for systematic generalization outside of the training distribution. However, human learners readily generalize in this way, e.g. by applying known grammatical rules to novel words. Inspired by work in neuroscience suggesting separate brain systems for syntactic and semantic processing, we implement a modification to standard approaches in neural machine translation, imposing an analogous separation. The novel model, which we call Syntactic Attention, substantially outperforms standard methods in deep learning on the SCAN dataset, a compositional generalization task, without any hand-engineered features or additional supervision. Our work suggests that separating syntactic from semantic learning may be a useful heuristic for capturing compositional structure.

Connected through hyperedge HE-1 (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"
    - "modeling question asking using neural program generation"
    - "babyai a platform to study the sample efficiency of grounded language learning"
    - "systematic generalization what is required and can it be learned"

Connected through hyperedge HE-2 (induced by source paper "learning compositional rules via neural program synthesis"):
  with members:
    - "learning to infer program sketches"
    - "learning to prove theorems via interacting with proof assistants"
    - "human few shot learning of compositional instructions"
    - "compositional generalization through meta sequence to sequence learning"
    - "write execute assess program synthesis with a repl"
    - "good enough compositional data augmentation"
    - "automatic program synthesis of long programs with a learned garbage collector"
    - "deep learning a critical appraisal"

Connected through hyperedge HE-3 (induced by source paper "synonymous generalization in sequence to sequence recurrent networks"):
  with members:
    - "memorize or generalize searching for a compositional rnn in a haystack"
    - "jump to better conclusions scan both left and right"
    - "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 "compositional neural machine translation by removing the lexicon from syntax"):
  with members:
    - "compound probabilistic context free grammars for grammar induction"
    - "human few shot learning of compositional instructions"
    - "compositional generalization through meta sequence to sequence learning"
    - "good enough compositional data augmentation"
    - "attention is all you need"
    - "data recombination for neural semantic parsing"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-5 (induced by source paper "closure assessing systematic generalization of clevr models"):
  with members:
    - "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"
    - "jump to better conclusions scan both left and right"
    - "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"
    - "mutual exclusivity as a challenge for deep neural networks"
    - "systematic generalization what is required and can it be learned"
    - "jump to better conclusions scan both left and right"
    - "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"
    - "improving text to sql evaluation methodology"
    - "systematic generalization what is required and can it be learned"

Connected through hyperedge HE-8 (induced by source paper "when low resource nlp meets unsupervised language model meta pretraining then meta learning for few shot text classification"):
  with members:
    - "to tune or not to tune adapting pretrained representations to diverse tasks"
    - "induction networks for few shot text classification"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "diverse few shot text classification with multiple metrics"
    - "attention is all you need"
    - "prototypical networks for few shot learning"
    - "learning to compare relation network for few shot learning"

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.LG(Machine Learning)