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: "positional encoding to control output sequence length"
  Abstract: Neural encoder-decoder models have been successful in natural language generation tasks. However, real applications of abstractive summarization must consider additional constraint that a generated summary should not exceed a desired length. In this paper, we propose a simple but effective extension of a sinusoidal positional encoding (Vaswani et al., 2017) to enable neural encoder-decoder model to preserves the length constraint. Unlike in previous studies where that learn embeddings representing each length, the proposed method can generate a text of any length even if the target length is not present in training data. The experimental results show that the proposed method can not only control the generation length but also improve the ROUGE scores.

Connected through hyperedge HE-1 (induced by source paper "controlling the output length of neural machine translation"):
  with members:
    - "revisiting character based neural machine translation with capacity and compression"
    - "achieving human parity on automatic chinese to english news translation"
    - "multi task neural models for translating between styles within and across languages"
    - "freezing subnetworks to analyze domain adaptation in neural machine translation"
    - "a survey of domain adaptation for neural machine translation"
    - "learning to segment inputs for nmt favors character level processing"
    - "scaling neural machine translation"
    - "neural machine translation into language varieties"

Connected through hyperedge HE-2 (induced by source paper "a large scale multi length headline corpus for improving length constrained headline generation model evaluation"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "biset bi directional selective encoding with template for abstractive summarization"
    - "a reinforced topic aware convolutional sequence to sequence model for abstractive text summarization"
    - "structure infused copy mechanisms for abstractive summarization"
    - "sequential copying networks"
    - "selective encoding for abstractive sentence summarization"
    - "attention is all you need"
    - "opennmt open source toolkit for neural machine translation"

Connected through hyperedge HE-3 (induced by source paper "knowledge enriched visual storytelling"):
  with members:
    - "visual story post editing"
    - "hierarchically structured reinforcement learning for topically coherent visual story generation"
    - "semstyle learning to generate stylised image captions using unaligned text"
    - "a skeleton based model for promoting coherence among sentences in narrative story generation"
    - "no metrics are perfect adversarial reward learning for visual storytelling"
    - "glac net glocal attention cascading networks for multi image cued story generation"
    - "hierarchical neural story generation"
    - "using inter sentence diverse beam search to reduce redundancy in visual storytelling"

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)