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: "cohesion based online actor critic reinforcement learning for mhealth intervention"
  Abstract: In the wake of the vast population of smart device users worldwide, mobile health (mHealth) technologies are hopeful to generate positive and wide influence on people's health. They are able to provide flexible, affordable and portable health guides to device users. Current online decision-making methods for mHealth assume that the users are completely heterogeneous. They share no information among users and learn a separate policy for each user. However, data for each user is very limited in size to support the separate online learning, leading to unstable policies that contain lots of variances. Besides, we find the truth that a user may be similar with some, but not all, users, and connected users tend to have similar behaviors. In this paper, we propose a network cohesion constrained (actor-critic) Reinforcement Learning (RL) method for mHealth. The goal is to explore how to share information among similar users to better convert the limited user information into sharper learned policies. To the best of our knowledge, this is the first online actor-critic RL for mHealth and first network cohesion constrained (actor-critic) RL method in all applications. The network cohesion is important to derive effective policies. We come up with a novel method to learn the network by using the warm start trajectory, which directly reflects the users' property. The optimization of our model is difficult and very different from the general supervised learning due to the indirect observation of values. As a contribution, we propose two algorithms for the proposed online RLs. Apart from mHealth, the proposed methods can be easily applied or adapted to other health-related tasks. Extensive experiment results on the HeartSteps dataset demonstrates that in a variety of parameter settings, the proposed two methods obtain obvious improvements over the state-of-the-art methods.

Connected through hyperedge HE-1 (induced by source paper "hyperspectral unmixing ground truth labeling datasets benchmark performances and survey"):
  with members:
    - "unsupervised nonlinear spectral unmixing based on a multilinear mixing model"
    - "effective warm start for the online actor critic reinforcement learning based mhealth intervention"
    - "low rank representation on riemannian manifold of square root densities"
    - "structured sparse method for hyperspectral unmixing"
    - "effective spectral unmixing via robust representation and learning based sparsity"
    - "spectral unmixing via data guided sparsity"
    - "robust hyperspectral unmixing with correntropy based metric"
    - "comparing measures of sparsity"

Connected through hyperedge HE-2 (induced by source paper "robust contextual bandit via the capped ell_ 2 norm"):
  with members:
    - "latent contextual bandits and their application to personalized recommendations for new users"
    - "structured sparse method for hyperspectral unmixing"
    - "10 000 times accelerated robust subset selection arss"
    - "effective spectral unmixing via robust representation and learning based sparsity"
    - "robust hyperspectral unmixing with correntropy based metric"
    - "a contextual bandit approach to personalized news article recommendation"

Connected through hyperedge HE-3 (induced by source paper "group driven reinforcement learning for personalized mhealth intervention"):
  with members:
    - "structured sparse method for hyperspectral unmixing"
    - "10 000 times accelerated robust subset selection arss"
    - "effective spectral unmixing via robust representation and learning based sparsity"
    - "robust hyperspectral unmixing with correntropy based metric"

Connected through hyperedge HE-4 (induced by source paper "effective warm start for the online actor critic reinforcement learning based mhealth intervention"):
  with members:
    - "structured sparse method for hyperspectral unmixing"
    - "robust hyperspectral unmixing with correntropy based metric"

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.LG(Machine Learning)