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: "model cards for model reporting"
  Abstract: Trained machine learning models are increasingly used to perform high-impact tasks in areas such as law enforcement, medicine, education, and employment. In order to clarify the intended use cases of machine learning models and minimize their usage in contexts for which they are not well suited, we recommend that released models be accompanied by documentation detailing their performance characteristics. In this paper, we propose a framework that we call model cards, to encourage such transparent model reporting. Model cards are short documents accompanying trained machine learning models that provide benchmarked evaluation in a variety of conditions, such as across different cultural, demographic, or phenotypic groups (e.g., race, geographic location, sex, Fitzpatrick skin type [15]) and intersectional groups (e.g., age and race, or sex and Fitzpatrick skin type) that are relevant to the intended application domains. Model cards also disclose the context in which models are intended to be used, details of the performance evaluation procedures, and other relevant information. While we focus primarily on human-centered machine learning models in the application fields of computer vision and natural language processing, this framework can be used to document any trained machine learning model. To solidify the concept, we provide cards for two supervised models: One trained to detect smiling faces in images, and one trained to detect toxic comments in text. We propose model cards as a step towards the responsible democratization of machine learning and related artificial intelligence technology, increasing transparency into how well artificial intelligence technology works. We hope this work encourages those releasing trained machine learning models to accompany model releases with similar detailed evaluation numbers and other relevant documentation.

Connected through hyperedge HE-1 (induced by source paper "explainability fact sheets a framework for systematic assessment of explainable approaches"):
  with members:
    - "blimey surrogate prediction explanations beyond lime"
    - "the dangers of post hoc interpretability unjustified counterfactual explanations"
    - "towards a characterization of explainable systems"
    - "why should you trust my explanation understanding uncertainty in lime explanations"
    - "personalized explanation in machine learning a conceptualization"
    - "face feasible and actionable counterfactual explanations"
    - "the challenge of crafting intelligible intelligence"
    - "a nutritional label for rankings"

Connected through hyperedge HE-2 (induced by source paper "closing the ai accountability gap defining an end to end framework for internal algorithmic auditing"):
  with members:
    - "principles alone cannot guarantee ethical ai"
    - "principles alone cannot guarantee ethical ai"
    - "diversity in faces"
    - "evolutionary computation and ai safety research problems impeding routine and safe real world application of evolution"
    - "the global landscape of ai ethics guidelines"
    - "improving fairness in machine learning systems what do industry practitioners need"
    - "a broader view on bias in automated decision making reflecting on epistemology and dynamics"
    - "examining gender and race bias in two hundred sentiment analysis systems"

Connected through hyperedge HE-3 (induced by source paper "trustworthy ai in the age of pervasive computing and big data"):
  with members:
    - "segment integrated gradients better attributions through regions"
    - "decision provenance harnessing data flow for accountable systems"
    - "attention based prototypical learning towards interpretable confident and robust deep neural networks"
    - "a machine learning approach to prevent malicious calls over telephony networks"
    - "a style based generator architecture for generative adversarial networks"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "blocking transferability of adversarial examples in black box learning systems"
    - "personalized and private peer to peer machine learning"

Connected through hyperedge HE-4 (induced by source paper "autoaiviz opening the blackbox of automated artificial intelligence with conditional parallel coordinates"):
  with members:
    - "effect of confidence and explanation on accuracy and trust calibration in ai assisted decision making"
    - "how do data science workers collaborate roles workflows and tools"
    - "questioning the ai informing design practices for explainable ai user experiences"
    - "how data scientistswork together with domain experts in scientific collaborations to find the right answer or to ask the right question"
    - "an admm based framework for automl pipeline configuration"
    - "conditional parallel coordinates"
    - "atmseer increasing transparency and controllability in automated machine learning"
    - "human ai collaboration in data science exploring data scientists perceptions of automated ai"

Connected through hyperedge HE-5 (induced by source paper "saving face investigating the ethical concerns of facial recognition auditing"):
  with members:
    - "diversity in faces"
    - "deep learning for face recognition pride or prejudiced"
    - "racial categories in machine learning"
    - "oxford handbook on ai ethics book chapter on race and gender"
    - "datasheets for datasets"
    - "preventing fairness gerrymandering auditing and learning for subgroup fairness"

Connected through hyperedge HE-6 (induced by source paper "the problem with metrics is a fundamental problem for ai"):
  with members:
    - "dark patterns at scale findings from a crawl of 11k shopping websites"
    - "auditing radicalization pathways on youtube"
    - "datasheets for datasets"

Connected through hyperedge HE-7 (induced by source paper "understanding artificial intelligence ethics and safety"):
  with members:
    - "a roadmap toward the resilient internet of things for cyber physical systems"
    - "a grounded interaction protocol for explainable artificial intelligence"
    - "automated rationale generation a technique for explainable ai and its effects on human perceptions"
    - "a framework for understanding unintended consequences of machine learning"
    - "spectrum data poisoning with adversarial deep learning"
    - "explaining explanations in ai"
    - "problem formulation and fairness"
    - "from what to how an initial review of publicly available ai ethics tools methods and research to translate principles into practices"

Connected through hyperedge HE-8 (induced by source paper "large scale long tailed recognition in an open world"):
  with members:
    - "large scale fine grained categorization and domain specific transfer learning"
    - "women also snowboard overcoming bias in captioning models"
    - "arcface additive angular margin loss for deep face recognition"
    - "episodic curiosity through reachability"
    - "incremental few shot learning with attention attractor networks"
    - "low shot learning from imaginary data"
    - "learning adversarially fair and transferable representations"
    - "dynamic few shot visual learning without forgetting"

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)