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 "controlling for confounders in multimodal emotion classification via adversarial learning"):
  Abstract: Various psychological factors affect how individuals express emotions. Yet, when we collect data intended for use in building emotion recognition systems, we often try to do so by creating paradigms that are designed just with a focus on eliciting emotional behavior. Algorithms trained with these types of data are unlikely to function outside of controlled environments because our emotions naturally change as a function of these other factors. In this work, we study how the multimodal expressions of emotion change when an individual is under varying levels of stress. We hypothesize that stress produces modulations that can hide the true underlying emotions of individuals and that we can make emotion recognition algorithms more generalizable by controlling for variations in stress. To this end, we use adversarial networks to decorrelate stress modulations from emotion representations. We study how stress alters acoustic and lexical emotional predictions, paying special attention to how modulations due to stress affect the transferability of learned emotion recognition models across domains. Our results show that stress is indeed encoded in trained emotion classifiers and that this encoding varies across levels of emotions and across the lexical and acoustic modalities. Our results also show that emotion recognition models that control for stress during training have better generalizability when applied to new domains, compared to models that do not control for stress during training. We conclude that is is necessary to consider the effect of extraneous psychological factors when building and testing emotion recognition models.

Includes members:
  Member "improving cross corpus speech emotion recognition with adversarial discriminative domain generalization addog":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "unsupervised adversarial domain adaptation for cross lingual speech emotion recognition")
  Member "adversarial training for satire detection controlling for confounding variables":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "do sentence interactions matter leveraging sentence level representations for fake news classification")
  Member "improving cross corpus speech emotion recognition with adversarial discriminative domain generalization addog":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "unsupervised adversarial domain adaptation for cross lingual speech emotion recognition")
  Member "muse ing on the impact of utterance ordering on crowdsourced emotion annotations":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "privacy enhanced multimodal neural representations for emotion recognition")
  Member "challenging common assumptions in the unsupervised learning of disentangled representations":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "nestedvae isolating common factors via weak supervision")
  Member "mitigating unwanted biases with adversarial learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "algorithmic fairness")
  Member "the priori emotion dataset linking mood to emotion detected in the wild":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "jointly aligning and predicting continuous emotion annotations")
  Member "adversarial removal of demographic attributes from text data":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "think locally act globally federated learning with local and global representations")

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)