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: "capturing long term temporal dependencies with convolutional networks for continuous emotion recognition"
  Abstract: The goal of continuous emotion recognition is to assign an emotion value to every frame in a sequence of acoustic features. We show that incorporating long-term temporal dependencies is critical for continuous emotion recognition tasks. To this end, we first investigate architectures that use dilated convolutions. We show that even though such architectures outperform previously reported systems, the output signals produced from such architectures undergo erratic changes between consecutive time steps. This is inconsistent with the slow moving ground-truth emotion labels that are obtained from human annotators. To deal with this problem, we model a downsampled version of the input signal and then generate the output signal through upsampling. Not only does the resulting downsampling/upsampling network achieve good performance, it also generates smooth output trajectories. Our method yields the best known audio-only performance on the RECOLA dataset.

Connected through hyperedge HE-1 (induced by source paper "controlling for confounders in multimodal emotion classification via adversarial learning"):
  with members:
    - "improving cross corpus speech emotion recognition with adversarial discriminative domain generalization addog"
    - "adversarial training for satire detection controlling for confounding variables"
    - "improving cross corpus speech emotion recognition with adversarial discriminative domain generalization addog"
    - "muse ing on the impact of utterance ordering on crowdsourced emotion annotations"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "mitigating unwanted biases with adversarial learning"
    - "the priori emotion dataset linking mood to emotion detected in the wild"
    - "adversarial removal of demographic attributes from text data"

Connected through hyperedge HE-2 (induced by source paper "jointly aligning and predicting continuous emotion annotations"):
  with members:
    - "trainable time warping aligning time series in the continuous time domain"
    - "the priori emotion dataset linking mood to emotion detected in the wild"
    - "progressive neural networks for transfer learning in emotion recognition"
    - "end to end multimodal emotion recognition using deep neural networks"
    - "learning representations of emotional speech with deep convolutional generative adversarial networks"
    - "emotion recognition by body movement representation on the manifold of symmetric positive definite matrices"
    - "avec 2016 depression mood and emotion recognition workshop and challenge"
    - "soundnet learning sound representations from unlabeled video"

Connected through hyperedge HE-3 (induced by source paper "improving cross corpus speech emotion recognition with adversarial discriminative domain generalization addog"):
  with members:
    - "on enhancing speech emotion recognition using generative adversarial networks"
    - "the priori emotion dataset linking mood to emotion detected in the wild"
    - "towards speech emotion recognition in the wild using aggregated corpora and deep multi task learning"
    - "adversarial discriminative domain adaptation"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "learning representations of emotional speech with deep convolutional generative adversarial networks"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "improving cross corpus speech emotion recognition with adversarial discriminative domain generalization addog"):
  with members:
    - "on enhancing speech emotion recognition using generative adversarial networks"
    - "the priori emotion dataset linking mood to emotion detected in the wild"
    - "towards speech emotion recognition in the wild using aggregated corpora and deep multi task learning"
    - "adversarial discriminative domain adaptation"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "learning representations of emotional speech with deep convolutional generative adversarial networks"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "transfer learning from sound representations for anger detection in speech"):
  with members:
    - "progressive neural networks for transfer learning in emotion recognition"
    - "end to end multimodal emotion recognition using deep neural networks"
    - "attentive convolutional neural network based speech emotion recognition a study on the impact of input features signal length and acted speech"
    - "soundnet learning sound representations from unlabeled video"
    - "yfcc100m the new data in multimedia research"
    - "adam a method for stochastic optimization"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-6 (induced by source paper "convolutional neural network based speech enhancement for cochlear implant recipients"):
  with members:
    - "quantifying cochlear implant users ability for speaker identification using ci auditory stimuli"
    - "jointly aligning and predicting continuous emotion annotations"
    - "progressive neural networks for transfer learning in emotion recognition"

Connected through hyperedge HE-7 (induced by source paper "muse ing on the impact of utterance ordering on crowdsourced emotion annotations"):
  with members:
    - "emotionlines an emotion corpus of multi party conversations"
    - "constance modeling annotation contexts to improve stance classification"

Connected through hyperedge HE-8 (induced by source paper "predicting expressive speaking style from text in end to end speech synthesis"):
  with members:
    - "style tokens unsupervised style modeling control and transfer in end to end speech synthesis"
    - "expressive speech synthesis via modeling expressions with variational autoencoder"
    - "towards end to end prosody transfer for expressive speech synthesis with tacotron"
    - "tacotron towards end to end speech synthesis"
    - "voice synthesis for in the wild speakers via a phonological loop"
    - "adam a method for stochastic optimization"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

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.SD(Sound)