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: "a formal framework to characterize interpretability of procedures"
  Abstract: We provide a novel notion of what it means to be interpretable, looking past the usual association with human understanding. Our key insight is that interpretability is not an absolute concept and so we define it relative to a target model, which may or may not be a human. We define a framework that allows for comparing interpretable procedures by linking it to important practical aspects such as accuracy and robustness. We characterize many of the current state-of-the-art interpretable methods in our framework portraying its general applicability.

Connected through hyperedge HE-1 (induced by source paper "teaching ai to explain its decisions using embeddings and multi task learning"):
  with members:
    - "ted teaching ai to explain its decisions"
    - "methods for interpreting and understanding deep neural networks"
    - "collaborative human ai chai evidence based interpretable melanoma classification in dermoscopic images"
    - "boolean decision rules via column generation"
    - "teaching meaningful explanations"
    - "explanation in artificial intelligence insights from the social sciences"
    - "a unified approach to interpreting model predictions"
    - "accountability of ai under the law the role of explanation"

Connected through hyperedge HE-2 (induced by source paper "teaching meaningful explanations"):
  with members:
    - "methods for interpreting and understanding deep neural networks"
    - "collaborative human ai chai evidence based interpretable melanoma classification in dermoscopic images"
    - "network dissection quantifying interpretability of deep visual representations"
    - "a causal framework for explaining the predictions of black box sequence to sequence models"
    - "explanation in artificial intelligence insights from the social sciences"
    - "challenges for transparency"
    - "accountability of ai under the law the role of explanation"
    - "explainable ai beware of inmates running the asylum or how i learnt to stop worrying and love the social and behavioural sciences"

Connected through hyperedge HE-3 (induced by source paper "ted teaching ai to explain its decisions"):
  with members:
    - "methods for interpreting and understanding deep neural networks"
    - "boolean decision rules via column generation"
    - "explanation in artificial intelligence insights from the social sciences"
    - "a unified approach to interpreting model predictions"
    - "accountability of ai under the law the role of explanation"
    - "explainable ai beware of inmates running the asylum or how i learnt to stop worrying and love the social and behavioural sciences"
    - "interpreting blackbox models via model extraction"
    - "generating visual 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.AI(Artificial Intelligence)