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: "generalization without systematicity on the compositional skills of sequence to sequence recurrent networks"
  Abstract: Humans can understand and produce new utterances effortlessly, thanks to their compositional skills. Once a person learns the meaning of a new verb "dax," he or she can immediately understand the meaning of "dax twice" or "sing and dax." In this paper, we introduce the SCAN domain, consisting of a set of simple compositional navigation commands paired with the corresponding action sequences. We then test the zero-shot generalization capabilities of a variety of recurrent neural networks (RNNs) trained on SCAN with sequence-to-sequence methods. We find that RNNs can make successful zero-shot generalizations when the differences between training and test commands are small, so that they can apply "mix-and-match" strategies to solve the task. However, when generalization requires systematic compositional skills (as in the "dax" example above), RNNs fail spectacularly. We conclude with a proof-of-concept experiment in neural machine translation, suggesting that lack of systematicity might be partially responsible for neural networks' notorious training data thirst.

Connected through hyperedge HE-1 (induced by source paper "hierarchical character embeddings learning phonological and semantic representations in languages of logographic origin using recursive neural networks"):
  with members:
    - "attentive mimicking better word embeddings by attending to informative contexts"
    - "phonology augmented statistical framework for machine transliteration using limited linguistic resources"
    - "chinese japanese unsupervised neural machine translation using sub character level information"
    - "neural machine translation of logographic languages using sub character level information"
    - "generalizing word embeddings using bag of subwords"
    - "dual long short term memory networks for sub character representation learning"
    - "extrapolation in nlp"
    - "deep contextualized word representations"

Connected through hyperedge HE-2 (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"
    - "jump to better conclusions scan both left and right"

Connected through hyperedge HE-3 (induced by source paper "evaluating logical generalization in graph neural networks"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "enhancing the transformer with explicit relational encoding for math problem solving"
    - "are powerful graph neural nets necessary a dissection on graph classification"
    - "clutrr a diagnostic benchmark for inductive reasoning from text"
    - "continual learning a comparative study on how to defy forgetting in classification tasks"
    - "graph neural tangent kernel fusing graph neural networks with graph kernels"
    - "pytorch an imperative style high performance deep learning library"
    - "on training recurrent neural networks for lifelong learning"

Connected through hyperedge HE-4 (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-5 (induced by source paper "does syntax need to grow on trees sources of hierarchical inductive bias in sequence to sequence networks"):
  with members:
    - "studying the inductive biases of rnns with synthetic variations of natural languages"
    - "a critical analysis of biased parsers in unsupervised parsing"
    - "cognitive science in the era of artificial intelligence a roadmap for reverse engineering the infant language learner"
    - "can you tell me how to get past sesame street sentence level pretraining beyond language modeling"
    - "on the practical computational power of finite precision rnns for language recognition"
    - "ordered neurons integrating tree structures into recurrent neural networks"
    - "revisiting the poverty of the stimulus hierarchical generalization without a hierarchical bias in recurrent neural networks"
    - "tree to tree neural networks for program translation"

Connected through hyperedge HE-6 (induced by source paper "exploiting language instructions for interpretable and compositional reinforcement learning"):
  with members:
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "babyai a platform to study the sample efficiency of grounded language learning"
    - "babyai first steps towards grounded language learning with a human in the loop"
    - "building generalizable agents with a realistic and rich 3d environment"
    - "learning to understand goal specifications by modelling reward"
    - "interactive grounded language acquisition and generalization in a 2d world"
    - "reverse curriculum generation for reinforcement learning"
    - "visualisation and diagnostic classifiers reveal how recurrent and recursive neural networks process hierarchical structure"

Connected through hyperedge HE-7 (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"
    - "compositional generalization in a deep seq2seq model by separating syntax and semantics"
    - "automatic program synthesis of long programs with a learned garbage collector"

Connected through hyperedge HE-8 (induced by source paper "environmental drivers of systematicity and generalization in a situated agent"):
  with members:
    - "human few shot learning of compositional instructions"
    - "the neuro symbolic concept learner interpreting scenes words and sentences from natural supervision"
    - "compositional generalization through meta sequence to sequence learning"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "systematic generalization what is required and can it be learned"
    - "understanding grounded language learning agents"

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)