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: "transparency motivations and challenges"
  Abstract: Transparency is often deemed critical to enable effective real-world deployment of intelligent systems. Yet the motivations for and benefits of different types of transparency can vary significantly depending on context, and objective measurement criteria are difficult to identify. We provide a brief survey, suggesting challenges and related concerns. We highlight and review settings where transparency may cause harm, discussing connections across privacy, multi-agent game theory, economics, fairness and trust.

Connected through hyperedge HE-1 (induced by source paper "model based and data driven strategies in medical image computing"):
  with members:
    - "deep learning cardiac motion analysis for human survival prediction"
    - "learning a probabilistic model for diffeomorphic registration"
    - "explicit topological priors for deep learning based image segmentation using persistent homology"
    - "towards federated learning at scale system design"
    - "quantifying and leveraging classification uncertainty for chest radiograph assessment"
    - "probabilistic motion modeling from medical image sequences application to cardiac cine mri"
    - "a deep learning framework for unsupervised affine and deformable image registration"
    - "unsupervised learning for fast probabilistic diffeomorphic registration"

Connected through hyperedge HE-2 (induced by source paper "towards explainable artificial intelligence"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "an evaluation of the human interpretability of explanation"
    - "interpretable deep learning in drug discovery"
    - "from clustering to cluster explanations via neural networks"
    - "uncovering convolutional neural network decisions for diagnosing multiple sclerosis on conventional mri using layer wise relevance propagation"
    - "unmasking clever hans predictors and assessing what machines really learn"
    - "understanding neural networks via feature visualization a survey"
    - "explaining and interpreting lstms"

Connected through hyperedge HE-3 (induced by source paper "explainable machine learning in deployment"):
  with members:
    - "the what if tool interactive probing of machine learning models"
    - "understanding impacts of high order loss approximations and features in deep learning interpretation"
    - "certifai counterfactual explanations for robustness transparency interpretability and fairness of artificial intelligence models"
    - "automatic model monitoring for data streams"
    - "why should you trust my explanation understanding uncertainty in lime explanations"
    - "neural network attributions a causal perspective"
    - "explaining explanations in ai"
    - "on the in fidelity and sensitivity for explanations"

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.CY(Computers and Society)