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 "learning unbiased representations via mutual information backpropagation"):
  Abstract: We are interested in learning data-driven representations that can generalize well, even when trained on inherently biased data. In particular, we face the case where some attributes (bias) of the data, if learned by the model, can severely compromise its generalization properties. We tackle this problem through the lens of information theory, leveraging recent findings for a differentiable estimation of mutual information. We propose a novel end-to-end optimization strategy, which simultaneously estimates and minimizes the mutual information between the learned representation and the data attributes. When applied on standard benchmarks, our model shows comparable or superior classification performance with respect to state-of-the-art approaches. Moreover, our method is general enough to be applicable to the problem of ``algorithmic fairness'', with competitive results.

Includes members:
  Member "on variational bounds of mutual information":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "toward interpretability of dual encoder models for dialogue response suggestions")
  Member "flexibly fair representation learning by disentanglement":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "nestedvae isolating common factors via weak supervision")
  Member "turning a blind eye explicit removal of biases and variation from deep neural network embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "demographic bias in biometrics a survey on an emerging challenge")
  Member "beyond local nash equilibria for adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "smoothness and stability in gans")
  Member "invariant representations without adversarial training":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "adversarial disentanglement with grouped observations")
  Member "learning not to learn training deep neural networks with biased data":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "don t judge an object by its context learning to overcome contextual bias")
  Member "mitigating unwanted biases with adversarial learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "algorithmic fairness")
  Member "generalizing across domains via cross gradient training":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "nestedvae isolating common factors via weak supervision")

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.CV(Computer Vision and Pattern Recognition)