human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "cnns found to jump around more skillfully than rnns compositional generalization in seq2seq convolutional networks"):
  Abstract: Lake and Baroni (2018) introduced the SCAN dataset probing the ability of seq2seq models to capture compositional generalizations, such as inferring the meaning of "jump around" 0-shot from the component words. Recurrent networks (RNNs) were found to completely fail the most challenging generalization cases. We test here a convolutional network (CNN) on these tasks, reporting hugely improved performance with respect to RNNs. Despite the big improvement, the CNN has however not induced systematic rules, suggesting that the difference between compositional and non-compositional behaviour is not clear-cut.

Includes members:
  Member "why self attention a targeted evaluation of neural machine translation architectures":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "meta embeddings based on self attention")
  Member "jump to better conclusions scan both left and right":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "linguistic generalization and compositionality in modern artificial neural networks")
  Member "rearranging the familiar testing compositional generalization in recurrent networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "linguistic generalization and compositionality in modern artificial neural networks")
  Member "revisiting the poverty of the stimulus hierarchical generalization without a hierarchical bias in recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "does syntax need to grow on trees sources of hierarchical inductive bias in sequence to sequence networks")
  Member "recurrent neural networks in linguistic theory revisiting pinker and prince 1988 and the past tense debate":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "tabula nearly rasa probing the linguistic knowledge of character level neural language models trained on unsegmented text")
  Member "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "blank language models")
  Member "attention is all you need":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "convolutional sequence to sequence learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a brief survey of multilingual neural machine translation")

Question: Please tell me which class the source paper 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)