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: "the priori emotion dataset linking mood to emotion detected in the wild"
  Abstract: Bipolar Disorder is a chronic psychiatric illness characterized by pathological mood swings associated with severe disruptions in emotion regulation. Clinical monitoring of mood is key to the care of these dynamic and incapacitating mood states. Frequent and detailed monitoring improves clinical sensitivity to detect mood state changes, but typically requires costly and limited resources. Speech characteristics change during both depressed and manic states, suggesting automatic methods applied to the speech signal can be effectively used to monitor mood state changes. However, speech is modulated by many factors, which renders mood state prediction challenging. We hypothesize that emotion can be used as an intermediary step to improve mood state prediction. This paper presents critical steps in developing this pipeline, including (1) a new in the wild emotion dataset, the PRIORI Emotion Dataset, collected from everyday smartphone conversational speech recordings, (2) activation/valence emotion recognition baselines on this dataset (PCC of 0.71 and 0.41, respectively), and (3) significant correlation between predicted emotion and mood state for individuals with bipolar disorder. This provides evidence and a working baseline for the use of emotion as a meta-feature for mood state monitoring.

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"
    - "adversarial removal of demographic attributes from text data"
    - "modeling and simultaneously removing bias via adversarial neural networks"

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"
    - "capturing long term temporal dependencies with convolutional networks for continuous emotion recognition"
    - "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 "privacy enhanced multimodal neural representations for emotion recognition"):
  with members:
    - "mirroring to build trust in digital assistants"
    - "differential privacy has disparate impact on model accuracy"
    - "racial bias in hate speech and abusive language detection datasets"
    - "muse ing on the impact of utterance ordering on crowdsourced emotion annotations"
    - "adversarial task specific privacy preservation under attribute attack"
    - "the measure and mismeasure of fairness a critical review of fair machine learning"
    - "privacy preserving neural representations of text"
    - "a marauder s map of security and privacy in machine learning"

Connected through hyperedge HE-4 (induced by source paper "linking emotions to behaviors through deep transfer learning"):
  with members:
    - "unsupervised online multitask learning of behavioral sentence embeddings"
    - "characterizing types of convolution in deep convolutional recurrent neural networks for robust speech emotion recognition"
    - "unsupervised latent behavior manifold learning from acoustic features audio2behavior"
    - "sparsely connected and disjointly trained deep neural networks for low resource behavioral annotation acoustic classification in couples therapy"
    - "emotional intensity analysis in bipolar subjects"
    - "neural speech recognizer acoustic to word lstm model for large vocabulary speech recognition"
    - "adam a method for stochastic optimization"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"

Connected through hyperedge HE-5 (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"
    - "capturing long term temporal dependencies with convolutional networks for continuous emotion recognition"
    - "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-6 (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"
    - "capturing long term temporal dependencies with convolutional networks for continuous emotion recognition"
    - "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-7 (induced by source paper "the ambiguous world of emotion representation"):
  with members:
    - "jointly aligning and predicting continuous emotion annotations"

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.HC(Human-Computer Interaction)