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 "multi modal multi scale deep learning for large scale image annotation"):
  Abstract: Large-scale image annotation is a challenging task in image content analysis, which aims to annotate each image of a very large dataset with multiple class labels. In this paper, we focus on two main issues in large-scale image annotation: 1) how to learn stronger features for multifarious images; 2) how to annotate an image with an automatically-determined number of class labels. To address the first issue, we propose a multi-modal multi-scale deep learning model for extracting descriptive features from multifarious images. Specifically, the visual features extracted by a multi-scale deep learning subnetwork are refined with the textual features extracted from social tags along with images by a simple multi-layer perception subnetwork. Since we have extracted very powerful features by multi-modal multi-scale deep learning, we simplify the second issue and decompose large-scale image annotation into multi-class classification and label quantity prediction. Note that the label quantity prediction subproblem can be implicitly solved when a recurrent neural network (RNN) model is used for image annotation. However, in this paper, we choose to explicitly solve this subproblem directly using our deep learning model, resulting in that we can pay more attention to deep feature learning. Experimental results demonstrate the superior performance of our model as compared to the state-of-the-art (including RNN-based models).

Includes members:
  Member "show and tell lessons learned from the 2015 mscoco image captioning challenge":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "aitpr attribute interaction tensor product representation for image caption")
  Member "learning from weak and noisy labels for semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "brain metastasis segmentation network trained with robustness to annotations with multiple false negatives")
  Member "tweet2vec character based distributed representations for social media":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "maskdga a black box evasion technique against dga classifiers and adversarial defenses")
  Member "a unified multi scale deep convolutional neural network for fast object detection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "ha ccn hierarchical attention based crowd counting network")
  Member "inception v4 inception resnet and the impact of residual connections on learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "stem seg spatio temporal embeddings for instance segmentation in videos")
  Member "annotation order matters recurrent image annotator for arbitrary length image tagging":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "orderless recurrent models for multi label classification")
  Member "semantic regularisation for recurrent image annotation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "improved hard example mining by discovering attribute based hard person identity")
  Member "cnn rnn a unified framework for multi label image classification":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learn to predict sets using feed forward neural networks")

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.CV(Computer Vision and Pattern Recognition)