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: "spatial temporal recurrent neural network for emotion recognition"
  Abstract: In this paper, we propose a novel deep learning framework, called spatial–temporal recurrent neural network (STRNN), to integrate the feature learning from both spatial and temporal information of signal sources into a unified spatial–temporal dependency model. In STRNN, to capture those spatially co-occurrent variations of human emotions, a multidirectional recurrent neural network (RNN) layer is employed to capture long-range contextual cues by traversing the spatial regions of each temporal slice along different directions. Then a bi-directional temporal RNN layer is further used to learn the discriminative features characterizing the temporal dependencies of the sequences, where sequences are produced from the spatial RNN layer. To further select those salient regions with more discriminative ability for emotion recognition, we impose sparse projection onto those hidden states of spatial and temporal domains to improve the model discriminant ability. Consequently, the proposed two-layer RNN model provides an effective way to make use of both spatial and temporal dependencies of the input signals for emotion recognition. Experimental results on the public emotion datasets of electroencephalogram and facial expression demonstrate the proposed STRNN method is more competitive over those state-of-the-art methods.

Connected through 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"):
  with members:
    - "is cqt more suitable for monaural speech separation than stft an empirical study"
    - "universal sound separation"
    - "furcanet an end to end deep gated convolutional long short term memory deep neural networks for single channel speech separation"
    - "divide and conquer a deep casa approach to talker independent monaural speaker separation"
    - "deep learning based phase reconstruction for speaker separation a trigonometric perspective"
    - "deep attractor network for single microphone speaker separation"
    - "tasnet time domain audio separation network for real time single channel speech separation"
    - "single channel multi speaker separation using deep clustering"

Connected through hyperedge HE-2 (induced by source paper "a survey on deep learning based brain computer interface recent advances and new frontiers"):
  with members:
    - "deep learning based electroencephalography analysis a systematic review"
    - "adversarial variational embedding for robust semi supervised learning"
    - "an automated system for epilepsy detection using eeg brain signals based on deep learning approach"
    - "on the classification of ssvep based dry eeg signals via convolutional neural networks"
    - "eegnet a compact convolutional neural network for eeg based brain computer interfaces"
    - "compact convolutional neural networks for classification of asynchronous steady state visual evoked potentials"
    - "hierarchical internal representation of spectral features in deep convolutional networks trained for eeg decoding"
    - "deep learning for fatigue estimation on the basis of multimodal human machine interactions"

Connected through hyperedge HE-3 (induced by source paper "deep learning for human affect recognition insights and new developments"):
  with members:
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "regularization for deep learning a taxonomy"
    - "exploring the limits of weakly supervised pretraining"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "progressive neural networks for transfer learning in emotion recognition"
    - "attention is all you need"
    - "understanding hidden memories of recurrent neural networks"
    - "end to end multimodal emotion recognition using deep neural networks"

Connected through hyperedge HE-4 (induced by source paper "recurrent embedding aggregation network for video face recognition"):
  with members:
    - "dependency aware attention control for unconstrained face recognition with image sets"
    - "multi prototype networks for unconstrained set based face recognition"
    - "video face recognition component wise feature aggregation network c fan"
    - "crystal loss and quality pooling for unconstrained face verification and recognition"
    - "trunk branch ensemble convolutional neural networks for video based face recognition"
    - "ghostvlad for set based face recognition"
    - "multicolumn networks for face recognition"
    - "comparator networks"

Connected through hyperedge HE-5 (induced by source paper "eeg based emotion recognition using regularized graph neural networks"):
  with members:
    - "simplifying graph convolutional networks"
    - "transfer adaptation learning a decade survey"
    - "a comprehensive survey on graph neural networks"
    - "graph neural networks a review of methods and applications"
    - "joint optimization framework for learning with noisy labels"
    - "a survey of unsupervised deep domain adaptation"
    - "how powerful are graph neural networks"
    - "deep label distribution learning with label ambiguity"

Connected through hyperedge HE-6 (induced by source paper "deep emotion facial expression recognition using attentional convolutional network"):
  with members:
    - "ad net audio visual convolutional neural network for advertisement detection in videos"
    - "local learning with deep and handcrafted features for facial expression recognition"
    - "mtbi identification from diffusion mr images using bag of adversarial visual features"
    - "an experimental study of deep convolutional features for iris recognition"
    - "incremental boosting convolutional neural network for facial action unit recognition"
    - "end to end multimodal emotion recognition using deep neural networks"
    - "mask r cnn"
    - "training deep networks for facial expression recognition with crowd sourced label distribution"

Connected through hyperedge HE-7 (induced by source paper "multi hop convolutions on weighted graphs"):
  with members:
    - "mixhop higher order graph convolutional architectures via sparsified neighborhood mixing"
    - "representation learning on graphs with jumping knowledge networks"
    - "geometric matrix completion with recurrent multi graph neural networks"
    - "geometric deep learning on graphs and manifolds using mixture model cnns"
    - "semi supervised classification with graph convolutional networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "deep residual learning for image recognition"
    - "multi scale context aggregation by dilated convolutions"

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.CV(Computer Vision and Pattern Recognition)