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: "functional transparency for structured data a game theoretic approach"
  Abstract: We provide a new approach to training neural models to exhibit transparency in a well-defined, functional manner. Our approach naturally operates over structured data and tailors the predictor, functionally, towards a chosen family of (local) witnesses. The estimation problem is setup as a co-operative game between an unrestricted predictor such as a neural network, and a set of witnesses chosen from the desired transparent family. The goal of the witnesses is to highlight, locally, how well the predictor conforms to the chosen family of functions, while the predictor is trained to minimize the highlighted discrepancy. We emphasize that the predictor remains globally powerful as it is only encouraged to agree locally with locally adapted witnesses. We analyze the effect of the proposed approach, provide example formulations in the context of deep graph and sequence models, and empirically illustrate the idea in chemical property prediction, temporal modeling, and molecule representation learning.

Connected through hyperedge HE-1 (induced by source paper "composing molecules with multiple property constraints"):
  with members:
    - "discrete object generation with reversible inductive construction"
    - "conditional molecular design with deep generative models"
    - "gnnexplainer generating explanations for graph neural networks"
    - "graph convolutional policy network for goal directed molecular graph generation"
    - "constrained graph variational autoencoders for molecule design"
    - "molecular hypergraph grammar with its application to molecular optimization"
    - "constrained generation of semantically valid graphs via regularizing variational autoencoders"
    - "graphvae towards generation of small graphs using variational autoencoders"

Connected through hyperedge HE-2 (induced by source paper "flexible and context specific ai explainability a multidisciplinary approach"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "towards robust locally linear deep networks"
    - "explaining a black box by using a deep variational information bottleneck approach"
    - "learning a sat solver from single bit supervision"
    - "safely entering the deep a review of verification and validation for machine learning and a challenge elicitation in the automotive industry"
    - "contextual explanation networks"
    - "smoothgrad removing noise by adding noise"
    - "a causal framework for explaining the predictions of black box sequence to sequence models"

Connected through hyperedge HE-3 (induced by source paper "a game theoretic approach to class wise selective rationalization"):
  with members:
    - "towards robust locally linear deep networks"
    - "rethinking cooperative rationalization introspective extraction and complement control"
    - "towards robust interpretability with self explaining neural networks"
    - "l shapley and c shapley efficient model interpretation for structured data"
    - "learning to explain an information theoretic perspective on model interpretation"
    - "deriving machine attention from human rationales"
    - "inferring and executing programs for visual reasoning"
    - "a causal framework for explaining the predictions of black box sequence to sequence models"

Connected through hyperedge HE-4 (induced by source paper "regularizing black box models for improved interpretability"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "model agnostic supervised local explanations"
    - "sanity checks for saliency maps"
    - "on the robustness of interpretability methods"
    - "contextual explanation networks"
    - "smoothgrad removing noise by adding noise"
    - "axiomatic attribution for deep networks"
    - "a unified approach to interpreting model predictions"

Connected through hyperedge HE-5 (induced by source paper "regularizing black box models for improved interpretability hill 2019 version"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "model agnostic supervised local explanations"
    - "sanity checks for saliency maps"
    - "on the robustness of interpretability methods"
    - "contextual explanation networks"
    - "smoothgrad removing noise by adding noise"
    - "axiomatic attribution for deep networks"
    - "a unified approach to interpreting model predictions"

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)