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 "robust actor critic contextual bandit for mobile health mhealth interventions"):
  Abstract: We consider the actor-critic contextual bandit for the mobile health (mHealth) intervention. State-of-the-art decision-making algorithms generally ignore the outliers in the dataset. In this paper, we propose a novel robust contextual bandit method for the mHealth. It can achieve the conflicting goal of reducing the influence of outliers while seeking for a similar solution compared with the state-of-the-art contextual bandit methods on the datasets without outliers. Such performance relies on two technologies: (1) the capped-$\ell_{2}$ norm; (2) a reliable method to set the thresholding hyper-parameter, which is inspired by one of the most fundamental techniques in the statistics. Although the model is non-convex and non-differentiable, we propose an effective reweighted algorithm and provide solid theoretical analyses. We prove that the proposed algorithm can find sufficiently decreasing points after each iteration and finally converges after a finite number of iterations. Extensive experiment results on two datasets demonstrate that our method can achieve almost identical results compared with state-of-the-art contextual bandit methods on the dataset without outliers, and significantly outperform those state-of-the-art methods on the badly noised dataset with outliers in a variety of parameter settings.

Includes members:
  Member "adaptive graph convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "transferring robustness for graph neural network against poisoning attacks")
  Member "name disambiguation in anonymized graphs using network embedding":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "fondue a framework for node disambiguation using network embeddings")
  Member "effective warm start for the online actor critic reinforcement learning based mhealth intervention":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "prolonets neural encoding human experts domain knowledge to warm start reinforcement learning")
  Member "recurrent reinforcement learning a hybrid approach":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "shaping belief states with generative environment models for rl")
  Member "median filtering is equivalent to sorting":
    also connected through other hyperedges:
      (none)
  Member "10 000 times accelerated robust subset selection arss":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "computer aided detection of squamous carcinoma of the cervix in whole slide images")
  Member "effective spectral unmixing via robust representation and learning based sparsity":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "hyperspectral unmixing ground truth labeling datasets benchmark performances and survey")
  Member "robust hyperspectral unmixing with correntropy based metric":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning with correntropy induced losses for regression with mixture of symmetric stable noise")

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)