human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "dynamic difficulty awareness training for continuous emotion prediction"
  Abstract: Time-continuous emotion prediction has become an increasingly compelling task in machine learning. Considerable efforts have been made to advance the performance of these systems. Nonetheless, the main focus has been the development of more sophisticated models and the incorporation of different expressive modalities (e.g., speech, face, and physiology). In this paper, motivated by the benefit of difficulty awareness in a human learning procedure, we propose a novel machine learning framework, namely, dynamic difficulty awareness training (DDAT), which sheds fresh light on the research—directly exploiting the difficulties in learning to boost the machine learning process. The DDAT framework consists of two stages: information retrieval and information exploitation. In the first stage, we make use of the reconstruction error of input features or the annotation uncertainty to estimate the difficulty of learning specific information. The obtained difficulty level is then used in tandem with original features to update the model input in a second learning stage with the expectation that the model can learn to focus on high difficulty regions of the learning process. We perform extensive experiments on a benchmark database REmote COLlaborative and affective to evaluate the effectiveness of the proposed framework. The experimental results show that our approach outperforms related baselines as well as other well-established time-continuous emotion prediction systems, which suggests that dynamically integrating the difficulty information for neural networks can help enhance the learning process.

Connected through hyperedge HE-1 (induced by source paper "emobed strengthening monomodal emotion recognition via training with crossmodal emotion embeddings"):
  with members:
    - "trunk branch ensemble convolutional neural networks for video based face recognition"
    - "the omg emotion behavior dataset"
    - "emotion recognition in speech using cross modal transfer in the wild"
    - "adversarial training in affective computing and sentiment analysis recent advances and perspectives"
    - "end to end multimodal emotion recognition using deep neural networks"
    - "avec 2016 depression mood and emotion recognition workshop and challenge"
    - "soundnet learning sound representations from unlabeled video"
    - "joint face detection and alignment using multitask cascaded convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "attention augmented end to end multi task learning for emotion prediction from speech"):
  with members:
    - "learning audio sequence representations for acoustic event classification"
    - "end to end multimodal emotion recognition using deep neural networks"
    - "tacotron towards end to end speech synthesis"
    - "an overview of multi task learning in deep neural networks"
    - "deep speech 2 end to end speech recognition in english and mandarin"
    - "end to end text dependent speaker verification"
    - "eesen end to end speech recognition using deep rnn models and wfst based decoding"
    - "recurrent models of visual attention"

Question: Please tell me which class the center node 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)