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: "still not systematic after all these years on the compositional skills of sequence to sequence recurrent networks"
  Abstract: Humans can understand and produce new utterances effortlessly, thanks to their systematic 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 generalize well 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, supporting the conjecture that lack of systematicity is an important factor explaining why neural networks need very large training sets.

Connected through hyperedge HE-1 (induced by source paper "the next decade in ai four steps towards robust artificial intelligence"):
  with members:
    - "memo a deep network for flexible combination of episodic memories"
    - "towards a human like open domain chatbot"
    - "learning higher order logic programs"
    - "solving rubik s cube with a robot hand"
    - "genesis generative scene inference and sampling with object centric latent representations"
    - "on the measure of intelligence"
    - "entity abstraction in visual model based reinforcement learning"
    - "emergent systematic generalization in a situated agent"

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 "on the impressive performance of randomly weighted encoders in summarization tasks"):
  with members:
    - "no training required exploring random encoders for sentence classification"
    - "syntax helps elmo understand semantics is syntax still relevant in a deep neural architecture for srl"
    - "a discourse aware attention model for abstractive summarization of long documents"
    - "senteval an evaluation toolkit for universal sentence representations"
    - "learning visually grounded sentence representations"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "get to the point summarization with pointer generator networks"
    - "visualisation and diagnostic classifiers reveal how recurrent and recursive neural networks process hierarchical structure"

Connected through hyperedge HE-4 (induced by source paper "node masking making graph neural networks generalize and scale better"):
  with members:
    - "inter sentence relation extraction with document level graph convolutional neural network"
    - "cluster gcn an efficient algorithm for training deep and large graph convolutional networks"
    - "abusive language detection with graph convolutional networks"
    - "relational inductive biases deep learning and graph networks"
    - "how powerful are graph neural networks"
    - "cayleynets graph convolutional neural networks with complex rational spectral filters"
    - "semi supervised classification with graph convolutional networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"

Connected through hyperedge HE-5 (induced by source paper "analyzing machine learned representations a natural language case study"):
  with members:
    - "right for the wrong reasons diagnosing syntactic heuristics in natural language inference"
    - "human few shot learning of compositional instructions"
    - "mutual exclusivity as a challenge for deep neural networks"
    - "breaking nli systems with sentences that require simple lexical inferences"
    - "analyzing compositionality sensitivity of nli models"
    - "analysis methods in neural language processing a survey"
    - "relational inductive biases deep learning and graph networks"
    - "deep learning a critical appraisal"

Connected through hyperedge HE-6 (induced by source paper "location attention for extrapolation to longer sequences"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "learning compositionally through attentive guidance"
    - "extrapolation in nlp"
    - "memorize or generalize searching for a compositional rnn in a haystack"
    - "generalisation in humans and deep neural networks"
    - "deep learning a critical appraisal"
    - "the fine line between linguistic generalization and failure in seq2seq attention models"
    - "attention is all you need"

Connected through hyperedge HE-7 (induced by source paper "pretrained language model transfer on neural named entity recognition in indonesian conversational texts"):
  with members:
    - "on the strength of character language models for multilingual named entity recognition"
    - "deep contextualized word representations"
    - "deep rnns encode soft hierarchical syntax"
    - "colorless green recurrent networks dream hierarchically"
    - "universal language model fine tuning for text classification"
    - "semi supervised multitask learning for sequence labeling"
    - "visualisation and diagnostic classifiers reveal how recurrent and recursive neural networks process hierarchical structure"
    - "transfer learning for named entity recognition with neural networks"

Connected through hyperedge HE-8 (induced by source paper "posing fair generalization tasks for natural language inference"):
  with members:
    - "help a dataset for identifying shortcomings of neural models in monotonicity reasoning"
    - "annotation artifacts in natural language inference data"
    - "breaking nli systems with sentences that require simple lexical inferences"
    - "hypothesis only baselines in natural language inference"
    - "stress testing neural models of natural language inference with multiply quantified sentences"
    - "analyzing compositionality sensitivity of nli models"
    - "stress test evaluation for natural language inference"
    - "evaluating compositionality in sentence embeddings"

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)