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 "privacy enhanced multimodal neural representations for emotion recognition"):
  Abstract: Many mobile applications and virtual conversational agents now aim to recognize and adapt to emotions. To enable this, data are transmitted from users' devices and stored on central servers. Yet, these data contain sensitive information that could be used by mobile applications without user's consent or, maliciously, by an eavesdropping adversary. In this work, we show how multimodal representations trained for a primary task, here emotion recognition, can unintentionally leak demographic information, which could override a selected opt-out option by the user. We analyze how this leakage differs in representations obtained from textual, acoustic, and multimodal data. We use an adversarial learning paradigm to unlearn the private information present in a representation and investigate the effect of varying the strength of the adversarial component on the primary task and on the privacy metric, defined here as the inability of an attacker to predict specific demographic information. We evaluate this paradigm on multiple datasets and show that we can improve the privacy metric while not significantly impacting the performance on the primary task. To the best of our knowledge, this is the first work to analyze how the privacy metric differs across modalities and how multiple privacy concerns can be tackled while still maintaining performance on emotion recognition.

Includes members:
  Member "mirroring to build trust in digital assistants":
    also connected through other hyperedges:
      (none)
  Member "differential privacy has disparate impact on model accuracy":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "salvaging federated learning by local adaptation")
  Member "racial bias in hate speech and abusive language detection datasets":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multilingual twitter corpus and baselines for evaluating demographic bias in hate speech recognition")
  Member "muse ing on the impact of utterance ordering on crowdsourced emotion annotations":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "controlling for confounders in multimodal emotion classification via adversarial learning")
  Member "adversarial task specific privacy preservation under attribute attack":
    also connected through other hyperedges:
      (none)
  Member "the measure and mismeasure of fairness a critical review of fair machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "algorithmic fairness")
  Member "privacy preserving neural representations of text":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "predictive biases in natural language processing models a conceptual framework and overview")
  Member "the priori emotion dataset linking mood to emotion detected in the wild":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "controlling for confounders in multimodal emotion classification via adversarial learning")

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)