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 "exploiting multi cnn features in cnn rnn based dimensional emotion recognition on the omg in the wild dataset"):
  Abstract: This paper presents a novel CNN-RNN based approach, which exploits multiple CNN features for dimensional emotion recognition in-the-wild, utilizing the One-Minute Gradual-Emotion (OMG-Emotion) dataset. Our approach includes first pre-training with the relevant and large in size, Aff-Wild and Aff-Wild2 emotion databases. Low-, mid- and high-level features are extracted from the trained CNN component and are exploited by RNN subnets in a multi-task framework. Their outputs constitute an intermediate level prediction; final estimates are obtained as the mean or median values of these predictions. Fusion of the networks is also examined for boosting the obtained performance, at Decision-, or at Model-level; in the latter case a RNN was used for the fusion. Our approach, although using only the visual modality, outperformed state-of-the-art methods that utilized audio and visual modalities. Some of our developments have been submitted to the OMG-Emotion Challenge, ranking second among the technologies which used only visual information for valence estimation; ranking third overall. Through extensive experimentation, we further show that arousal estimation is greatly improved when low-level features are combined with high-level ones.

Includes members:
  Member "expression affect action unit recognition aff wild2 multi task learning and arcface":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "lossless attention in convolutional networks for facial expression recognition in the wild")
  Member "affectnet a database for facial expression valence and arousal computing in the wild":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "cascade ef gan progressive facial expression editing with local focuses")
  Member "deep affect prediction in the wild aff wild database and challenge deep architectures and beyond":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "lossless attention in convolutional networks for facial expression recognition in the wild")
  Member "training deep neural networks with different datasets in the wild the emotion recognition paradigm":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "emotion generation and recognition a stargan approach")
  Member "the omg emotion behavior dataset":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multitask emotion recognition with incomplete labels")
  Member "a multi component cnn rnn approach for dimensional emotion recognition in the wild":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "analysing affective behavior in the first abaw 2020 competition")
  Member "a deep network for arousal valence emotion prediction with acoustic visual cues":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a personalized affective memory neural model for improving emotion recognition")
  Member "audeering s approach to the one minute gradual emotion challenge":
    also connected through other hyperedges:
      (none)

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.LG(Machine Learning)