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: "learning compositionally through attentive guidance"
  Abstract: While neural network models have been successfully applied to domains that require substantial generalisation skills, recent studies have implied that they struggle when solving the task they are trained on requires inferring its underlying compositional structure. In this paper, we introduce Attentive Guidance, a mechanism to direct a sequence to sequence model equipped with attention to find more compositional solutions. We test it on two tasks, devised precisely to assess the compositional capabilities of neural models, and we show that vanilla sequence to sequence models with attention overfit the training distribution, while the guided versions come up with compositional solutions that fit the training and testing distributions almost equally well. Moreover, the learned solutions generalise even in cases where the training and testing distributions strongly diverge. In this way, we demonstrate that sequence to sequence models are capable of finding compositional solutions without requiring extra components. These results helps to disentangle the causes for the lack of systematic compositionality in neural networks, which can in turn fuel future work.

Connected through hyperedge HE-1 (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-2 (induced by source paper "location attention for extrapolation to longer sequences"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "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"
    - "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-3 (induced by source paper "transcoding compositionally using attention to find more generalizable solutions"):
  with members:
    - "on the realization of compositionality in neural networks"
    - "memorize or generalize searching for a compositional rnn in a haystack"
    - "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-4 (induced by source paper "assessing incrementality in sequence to sequence models"):
  with members:
    - "deep text to speech system with seq2seq model"
    - "transcoding compositionally using attention to find more generalizable solutions"
    - "on the convergence of adam and beyond"
    - "incremental decoding and training methods for simultaneous translation in neural machine translation"
    - "story ending generation with incremental encoding and commonsense knowledge"
    - "rearranging the familiar testing compositional generalization in recurrent networks"
    - "closed book training to improve summarization encoder memory"
    - "what you can cram into a single vector probing sentence embeddings for linguistic properties"

Connected through hyperedge HE-5 (induced by source paper "on the realization of compositionality in neural networks"):
  with members:
    - "transcoding compositionally using attention to find more generalizable solutions"
    - "memorize or generalize searching for a compositional rnn in a haystack"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "limitations in learning an interpreted language with recurrent models"):
  with members:
    - "memorize or generalize searching for a compositional rnn in a haystack"
    - "jointly learning sentence embeddings and syntax with unsupervised tree lstms"
    - "generalization without systematicity on the compositional skills of sequence to sequence recurrent networks"

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)