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 "assessing incrementality in sequence to sequence models"):
  Abstract: Since their inception, encoder-decoder models have successfully been applied to a wide array of problems in computational linguistics. The most recent successes are predominantly due to the use of different variations of attention mechanisms, but their cognitive plausibility is questionable. In particular, because past representations can be revisited at any point in time, attention-centric methods seem to lack an incentive to build up incrementally more informative representations of incoming sentences. This way of processing stands in stark contrast with the way in which humans are believed to process language: continuously and rapidly integrating new information as it is encountered. In this work, we propose three novel metrics to assess the behavior of RNNs with and without an attention mechanism and identify key differences in the way the different model types process sentences.

Includes members:
  Member "deep text to speech system with seq2seq model":
    also connected through other hyperedges:
      (none)
  Member "transcoding compositionally using attention to find more generalizable solutions":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the compositionality of neural networks integrating symbolism and connectionism")
  Member "on the convergence of adam and beyond":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "disentangling adaptive gradient methods from learning rates")
  Member "learning compositionally through attentive guidance":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "the compositionality of neural networks integrating symbolism and connectionism")
  Member "incremental decoding and training methods for simultaneous translation in neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning coupled policies for simultaneous machine translation")
  Member "story ending generation with incremental encoding and commonsense knowledge":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a knowledge enhanced pretraining model for commonsense story generation")
  Member "rearranging the familiar testing compositional generalization in recurrent networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "linguistic generalization and compositionality in modern artificial neural networks")
  Member "closed book training to improve summarization encoder memory":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "neural text summarization a critical evaluation")

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)