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 "adversarial reconstruction for multi modal machine translation"):
  Abstract: Even with the growing interest in problems at the intersection of Computer Vision and Natural Language, grounding (i.e. identifying) the components of a structured description in an image still remains a challenging task. This contribution aims to propose a model which learns grounding by reconstructing the visual features for the Multi-modal translation task. Previous works have partially investigated standard approaches such as regression methods to approximate the reconstruction of a visual input. In this paper, we propose a different and novel approach which learns grounding by adversarial feedback. To do so, we modulate our network following the recent promising adversarial architectures and evaluate how the adversarial response from a visual reconstruction as an auxiliary task helps the model in its learning. We report the highest scores in term of BLEU and METEOR metrics on the different datasets.

Includes members:
  Member "lium cvc submissions for wmt18 multimodal translation task":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "debiasing word embeddings improves multimodal machine translation")
  Member "unsupervised image captioning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "c textgen conditional text generation for harmonious human machine interaction")
  Member "spectral normalization for generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "umons submission for wmt18 multimodal translation task":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "bringing back simplicity and lightliness into neural image captioning")
  Member "imagination improves multimodal translation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "countering language drift via visual grounding")
  Member "modulating and attending the source image during encoding improves multimodal translation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "visual reasoning with multi hop feature modulation")
  Member "multimodal compact bilinear pooling for multimodal neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "neuron interaction based representation composition for neural machine translation")
  Member "doubly attentive decoder for multi modal neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "probing contextualized sentence representations with visual awareness")

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)