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: "a simple loss function for improving the convergence and accuracy of visual question answering models"
  Abstract: Visual question answering as recently proposed multimodal learning task has enjoyed wide attention from the deep learning community. Lately, the focus was on developing new representation fusion methods and attention mechanisms to achieve superior performance. On the other hand, very little focus has been put on the models' loss function, arguably one of the most important aspects of training deep learning models. The prevailing practice is to use cross entropy loss function that penalizes the probability given to all the answers in the vocabulary except the single most common answer for the particular question. However, the VQA evaluation function compares the predicted answer with all the ground-truth answers for the given question and if there is a matching, a partial point is given. This causes a discrepancy between the model's cross entropy loss and the model's accuracy as calculated by the VQA evaluation function. In this work, we propose a novel loss, termed as soft cross entropy, that considers all ground-truth answers and thus reduces the loss-accuracy discrepancy. The proposed loss leads to an improved training convergence of VQA models and an increase in accuracy as much as 1.6%.

Connected through hyperedge HE-1 (induced by source paper "scene graph reasoning with prior visual relationship for visual question answering"):
  with members:
    - "graph neural networks a review of methods and applications"
    - "iterative visual reasoning beyond convolutions"
    - "neural symbolic vqa disentangling reasoning from vision and language understanding"
    - "straight to the facts learning knowledge base retrieval for factual visual question answering"
    - "large scale visual relationship understanding"
    - "compositional attention networks for machine reasoning"
    - "out of the box reasoning with graph convolution nets for factual visual question answering"
    - "exploring visual relationship for image captioning"

Connected through hyperedge HE-2 (induced by source paper "learning answer embeddings for visual question answering"):
  with members:
    - "beyond bilinear generalized multi modal factorized high order pooling for visual question answering"
    - "visual question answering datasets algorithms and future challenges"
    - "show ask attend and answer a strong baseline for visual question answering"
    - "mutan multimodal tucker fusion for visual question answering"
    - "being negative but constructively lessons learnt from creating better visual question answering datasets"
    - "survey of visual question answering datasets and techniques"
    - "multi modal factorized bilinear pooling with co attention learning for visual question answering"
    - "aggregated residual transformations for deep neural networks"

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.CV(Computer Vision and Pattern Recognition)