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 "feature level and model level audiovisual fusion for emotion recognition in the wild"):
  Abstract: Emotion recognition plays an important role in human-computer interaction (HCI) and has been extensively studied for decades. Although tremendous improvements have been achieved for posed expressions, recognizing human emotions in "close-to-real-world" environments remains a challenge. In this paper, we proposed two strategies to fuse information extracted from different modalities, i.e., audio and visual. Specifically, we utilized LBP-TOP, an ensemble of CNNs, and a bi-directional LSTM (BLSTM) to extract features from the visual channel, and the OpenSmile toolkit to extract features from the audio channel. Two kinds of fusion methods, i,e., feature-level fusion and model-level fusion, were developed to utilize the information extracted from the two channels. Experimental results on the EmotiW2018 AFEW dataset have shown that the proposed fusion methods outperform the baseline methods significantly and achieve better or at least comparable performance compared with the state-of-the-art methods, where the model-level fusion performs better when one of the channels totally fails.

Includes members:
  Member "identity free facial expression recognition using conditional generative adversarial network":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "facial expression recognition using human to animated character expression translation")
  Member "deep texture manifold for ground terrain recognition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "histogram layers for texture analysis")
  Member "probabilistic attribute tree in convolutional neural networks for facial expression recognition":
    also connected through other hyperedges:
      (none)
  Member "emotiw 2018 audio video student engagement and group level affect prediction":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "region attention networks for pose and occlusion robust facial expression recognition")
  Member "island loss for learning discriminative features in facial expression recognition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "region attention networks for pose and occlusion robust facial expression recognition")
  Member "convolutional neural networks pretrained on large face recognition datasets for emotion classification from video":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep affect prediction in the wild aff wild database and challenge deep architectures and beyond")
  Member "temporal multimodal fusion for video emotion classification in the wild":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "factorized multimodal transformer for multimodal sequential learning")
  Member "optimizing filter size in convolutional neural networks for facial action unit recognition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "h cnn spatial hashing based cnn for 3d shape analysis")

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)