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 "the fine line between linguistic generalization and failure in seq2seq attention models"):
  Abstract: Seq2Seq based neural architectures have become the go-to architecture to apply to sequence to sequence language tasks. Despite their excellent performance on these tasks, recent work has noted that these models usually do not fully capture the linguistic structure required to generalize beyond the dense sections of the data distribution \cite{ettinger2017towards}, and as such, are likely to fail on samples from the tail end of the distribution (such as inputs that are noisy \citep{belkinovnmtbreak} or of different lengths \citep{bentivoglinmtlength}). In this paper, we look at a model's ability to generalize on a simple symbol rewriting task with a clearly defined structure. We find that the model's ability to generalize this structure beyond the training distribution depends greatly on the chosen random seed, even when performance on the standard test set remains the same. This suggests that a model's ability to capture generalizable structure is highly sensitive. Moreover, this sensitivity may not be apparent when evaluating it on standard test sets.

Includes members:
  Member "memorize or generalize searching for a compositional rnn in a haystack":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "synonymous generalization in sequence to sequence recurrent 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-2 (induced by source paper "does syntax need to grow on trees sources of hierarchical inductive bias in sequence to sequence networks")
  Member "still not systematic after all these years on the compositional skills of sequence to sequence recurrent networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "the next decade in ai four steps towards robust artificial intelligence")
  Member "do latent tree learning models identify meaningful structure in sentences":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "hierarchical character embeddings learning phonological and semantic representations in languages of logographic origin using recursive neural networks")
  Member "synthetic and natural noise both break neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "humpty dumpty controlling word meanings via corpus poisoning")
  Member "towards linguistically generalizable nlp systems a workshop and shared task":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "beat the ai investigating adversarial human annotations for reading comprehension")
  Member "what do neural machine translation models learn about morphology":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "bi decoder augmented network for neural machine translation")
  Member "effective approaches to attention based neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "attention over parameters for dialogue systems")

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)