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 "deep emotion facial expression recognition using attentional convolutional network"):
  Abstract: Facial expression recognition has been an active research area over the past few decades, and it is still challenging due to the high intra-class variation. #R##N#Traditional approaches for this problem rely on hand-crafted features such as SIFT, HOG and LBP, followed by a classifier trained on a database of images or videos. #R##N#Most of these works perform reasonably well on datasets of images captured in a controlled condition, but fail to perform as good on more challenging datasets with more image variation and partial faces. #R##N#In recent years, several works proposed an end-to-end framework for facial expression recognition, using deep learning models. #R##N#Despite the better performance of these works, there still seems to be a great room for improvement. #R##N#In this work, we propose a deep learning approach based on attentional convolutional network, which is able to focus on important parts of the face, and achieves significant improvement over previous models on multiple datasets, including FER-2013, CK+, FERG, and JAFFE. #R##N#We also use a visualization technique which is able to find important face regions for detecting different emotions, based on the classifier's output. #R##N#Through experimental results, we show that different emotions seems to be sensitive to different parts of the face.

Includes members:
  Member "spatial temporal recurrent neural network for emotion recognition":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "la furca iterative context aware end to end monaural speech separation based on dual path deep parallel inter intra bi lstm with attention")
  Member "ad net audio visual convolutional neural network for advertisement detection in videos":
    also connected through other hyperedges:
      (none)
  Member "local learning with deep and handcrafted features for facial expression recognition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning to augment expressions for few shot fine grained facial expression recognition")
  Member "mtbi identification from diffusion mr images using bag of adversarial visual features":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deep sentiment sentiment analysis using ensemble of cnn and bi lstm models")
  Member "an experimental study of deep convolutional features for iris recognition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep learning based feature extraction in iris recognition use existing models fine tune or train from scratch")
  Member "incremental boosting convolutional neural network for facial action unit recognition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "robust regression via deep negative correlation learning")
  Member "end to end multimodal emotion recognition using deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "multi task learning with auxiliary speaker identification for conversational emotion recognition")
  Member "mask r cnn":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional 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)