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: "memorize or generalize searching for a compositional rnn in a haystack"
  Abstract: Neural networks are very powerful learning systems, but they do not readily generalize from one task to the other. This is partly due to the fact that they do not learn in a compositional way, that is, by discovering skills that are shared by different tasks, and recombining them to solve new problems. In this paper, we explore the compositional generalization capabilities of recurrent neural networks (RNNs). We first propose the lookup table composition domain as a simple setup to test compositional behaviour and show that it is theoretically possible for a standard RNN to learn to behave compositionally in this domain when trained with standard gradient descent and provided with additional supervision. We then remove this additional supervision and perform a search over a large number of model initializations to investigate the proportion of RNNs that can still converge to a compositional solution. We discover that a small but non-negligible proportion of RNNs do reach partial compositional solutions even without special architectural constraints. This suggests that a combination of gradient descent and evolutionary strategies directly favouring the minority models that developed more compositional approaches might suffice to lead standard RNNs towards compositional solutions.

Connected through hyperedge HE-1 (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"
    - "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-2 (induced by source paper "the compositionality of neural networks integrating symbolism and connectionism"):
  with members:
    - "unsupervised recurrent neural network grammars"
    - "cnns found to jump around more skillfully than rnns compositional generalization in seq2seq convolutional networks"
    - "assessing bert s syntactic abilities"
    - "bert rediscovers the classical nlp pipeline"
    - "measuring compositionality in representation learning"
    - "analyzing the structure of attention in a transformer language model"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "the emergence of number and syntax units in lstm language models"

Connected through hyperedge HE-3 (induced by source paper "capacity bandwidth and compositionality in emergent language learning"):
  with members:
    - "linguistic generalization and compositionality in modern artificial neural networks"
    - "on the interaction between supervision and self play in emergent communication"
    - "anti efficient encoding in emergent communication"
    - "the compositionality of neural networks integrating symbolism and connectionism"
    - "word order biases in deep agent emergent communication"
    - "measuring compositionality in representation learning"
    - "countering language drift via visual grounding"
    - "compositional generalization through meta sequence to sequence learning"

Connected through hyperedge HE-4 (induced by source paper "berts of a feather do not generalize together large variability in generalization across models with similar test set performance"):
  with members:
    - "what bert is not lessons from a new suite of psycholinguistic diagnostics for language models"
    - "visualizing and measuring the geometry of bert"
    - "assessing bert s syntactic abilities"
    - "bert rediscovers the classical nlp pipeline"
    - "correlating neural and symbolic representations of language"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "multi task deep neural networks for natural language understanding"
    - "open sesame getting inside bert s linguistic knowledge"

Connected through hyperedge HE-5 (induced by source paper "compositional generalization for primitive substitutions"):
  with members:
    - "human few shot learning of compositional instructions"
    - "efficient lifelong learning with a gem"
    - "jump to better conclusions scan both left and right"
    - "on the practical computational power of finite precision rnns for language recognition"
    - "rearranging the familiar testing compositional generalization in recurrent networks"
    - "automatically composing representation transformations as a means for generalization"
    - "memory aware synapses learning what not to forget"
    - "attention is all you need"

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"
    - "generalisation in humans and deep neural networks"
    - "deep learning a critical appraisal"
    - "the fine line between linguistic generalization and failure in seq2seq attention models"
    - "still not systematic after all these years on the compositional skills of sequence to sequence recurrent networks"
    - "attention is all you need"

Connected through hyperedge HE-7 (induced by source paper "transcoding compositionally using attention to find more generalizable solutions"):
  with members:
    - "on the realization of compositionality in neural networks"
    - "learning compositionally through attentive guidance"
    - "can lstm learn to capture agreement the case of basque"
    - "compositional attention networks for machine reasoning"
    - "rearranging the familiar testing compositional generalization in recurrent networks"
    - "attention is all you need"
    - "visualisation and diagnostic classifiers reveal how recurrent and recursive neural networks process hierarchical structure"
    - "convolutional sequence to sequence learning"

Connected through hyperedge HE-8 (induced by source paper "on the realization of compositionality in neural networks"):
  with members:
    - "transcoding compositionally using attention to find more generalizable solutions"
    - "learning compositionally through attentive guidance"
    - "what is one grain of sand in the desert analyzing individual neurons in deep nlp models"
    - "a unified approach to interpreting model predictions"
    - "visualisation and diagnostic classifiers reveal how recurrent and recursive neural networks process hierarchical structure"
    - "visualizing and understanding recurrent networks"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"

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.AI(Artificial Intelligence)