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 "emobed strengthening monomodal emotion recognition via training with crossmodal emotion embeddings"):
  Abstract: Despite remarkable advances in emotion recognition, they are severely restrained from either the essentially limited property of the employed single modality, or the synchronous presence of all involved multiple modalities. Motivated by this, we propose a novel crossmodal emotion embedding framework called EmoBed, which aims to leverage the knowledge from other auxiliary modalities to improve the performance of an emotion recognition system at hand. The framework generally includes two main learning components, i. e., joint multimodal training and crossmodal training. Both of them tend to explore the underlying semantic emotion information but with a shared recognition network or with a shared emotion embedding space, respectively. In doing this, the enhanced system trained with this approach can efficiently make use of the complementary information from other modalities. Nevertheless, the presence of these auxiliary modalities is not demanded during inference. To empirically investigate the effectiveness and robustness of the proposed framework, we perform extensive experiments on the two benchmark databases RECOLA and OMG-Emotion for the tasks of dimensional emotion regression and categorical emotion classification, respectively. The obtained results show that the proposed framework significantly outperforms related baselines in monomodal inference, and are also competitive or superior to the recently reported systems, which emphasises the importance of the proposed crossmodal learning for emotion recognition.

Includes members:
  Member "dynamic difficulty awareness training for continuous emotion prediction":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "attention augmented end to end multi task learning for emotion prediction from speech")
  Member "trunk branch ensemble convolutional neural networks for video based face recognition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "dual triplet metric learning for unsupervised domain adaptation in video based face recognition")
  Member "the omg emotion behavior dataset":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multitask emotion recognition with incomplete labels")
  Member "emotion recognition in speech using cross modal transfer in the wild":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "semantic object prediction and spatial sound super resolution with binaural sounds")
  Member "adversarial training in affective computing and sentiment analysis recent advances and perspectives":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "adversarial based neural networks for affect estimations in the wild")
  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 "avec 2016 depression mood and emotion recognition workshop and challenge":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep learning for human affect recognition insights and new developments")
  Member "soundnet learning sound representations from unlabeled video":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning spatiotemporal features via video and text pair discrimination")

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)