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: "explanations based on the missing towards contrastive explanations with pertinent negatives"
  Abstract: In this paper we propose a novel method that provides contrastive explanations justifying the classification of an input by a black box classifier such as a deep neural network. Given an input we find what should be minimally and sufficiently present (viz. important object pixels in an image) to justify its classification and analogously what should be minimally and necessarily \emph{absent} (viz. certain background pixels). We argue that such explanations are natural for humans and are used commonly in domains such as health care and criminology. What is minimally but critically \emph{absent} is an important part of an explanation, which to the best of our knowledge, has not been touched upon by current explanation methods that attempt to explain predictions of neural networks. We validate our approach on three real datasets obtained from diverse domains; namely, a handwritten digits dataset MNIST, a large procurement fraud dataset and an fMRI brain imaging dataset. In all three cases, we witness the power of our approach in generating precise explanations that are also easy for human experts to understand and evaluate.

Connected through hyperedge HE-1 (induced by source paper "questioning the ai informing design practices for explainable ai user experiences"):
  with members:
    - "explaining machine learning classifiers through diverse counterfactual explanations"
    - "a grounded interaction protocol for explainable artificial intelligence"
    - "personalized explanation in machine learning"
    - "on human predictions with explanations and predictions of machine learning models a case study on deception detection"
    - "improving fairness in machine learning systems what do industry practitioners need"
    - "explaining models an empirical study of how explanations impact fairness judgment"
    - "the challenge of crafting intelligible intelligence"
    - "metrics for explainable ai challenges and prospects"

Connected through hyperedge HE-2 (induced by source paper "chexplain enabling physicians to explore and understanddata driven ai enabled medical imaging analysis"):
  with members:
    - "outlining the design space of explainable intelligent systems for medical diagnosis"
    - "human centered tools for coping with imperfect algorithms during medical decision making"
    - "explanation methods in deep learning users values concerns and challenges"
    - "explainable cardiac pathology classification on cine mri with motion characterization by semi supervised learning of apparent flow"
    - "generating counterfactual explanations with natural language"
    - "towards interpretable r cnn by unfolding latent structures"
    - "explanation in artificial intelligence insights from the social sciences"
    - "a simple neural network module for relational reasoning"

Connected through hyperedge HE-3 (induced by source paper "algorithmic recourse from counterfactual explanations to interventions"):
  with members:
    - "the what if tool interactive probing of machine learning models"
    - "the hidden assumptions behind counterfactual explanations and principal reasons"
    - "explaining machine learning classifiers through diverse counterfactual explanations"
    - "certifai counterfactual explanations for robustness transparency interpretability and fairness of artificial intelligence models"
    - "towards realistic individual recourse and actionable explanations in black box decision making systems"
    - "preserving causal constraints in counterfactual explanations for machine learning classifiers"
    - "face feasible and actionable counterfactual explanations"
    - "efficient search for diverse coherent explanations"

Connected through hyperedge HE-4 (induced by source paper "convex density constraints for computing plausible counterfactual explanations"):
  with members:
    - "efficient computation of counterfactual explanations of lvq models"
    - "a survey on bias and fairness in machine learning"
    - "face feasible and actionable counterfactual explanations"
    - "interpretable counterfactual explanations guided by prototypes"
    - "a survey on explainable artificial intelligence xai towards medical xai"
    - "on the computation of counterfactual explanations a survey"
    - "generating contrastive explanations with monotonic attribute functions"
    - "local rule based explanations of black box decision systems"

Connected through hyperedge HE-5 (induced by source paper "explaining groups of points in low dimensional representations"):
  with members:
    - "from clustering to cluster explanations via neural networks"
    - "model agnostic contrastive explanations for structured data"
    - "counterfactual visual explanations"
    - "model agnostic supervised local explanations"
    - "interpreting neural network judgments via minimal stable and symbolic corrections"
    - "axiomatic attribution for deep networks"
    - "a unified approach to interpreting model predictions"
    - "the mythos of model interpretability"

Connected through hyperedge HE-6 (induced by source paper "the hidden assumptions behind counterfactual explanations and principal reasons"):
  with members:
    - "explaining machine learning classifiers through diverse counterfactual explanations"
    - "efficient search for diverse coherent explanations"
    - "model agnostic counterfactual explanations for consequential decisions"
    - "delayed impact of fair machine learning"
    - "interpretable credit application predictions with counterfactual explanations"
    - "how do classifiers induce agents to invest effort strategically"
    - "generating counterfactual explanations with natural language"
    - "counterfactual explanations without opening the black box automated decisions and the gdpr"

Connected through hyperedge HE-7 (induced by source paper "learning global transparent models from local contrastive explanations"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "explaining machine learning classifiers through diverse counterfactual explanations"
    - "model agnostic contrastive explanations for structured data"
    - "improving simple models with confidence profiles"
    - "a unified approach to interpreting model predictions"
    - "interpreting blackbox models via model extraction"
    - "generating visual explanations"
    - "why should i trust you explaining the predictions of any classifier"

Connected through hyperedge HE-8 (induced by source paper "human to ai coach improving human inputs to ai systems"):
  with members:
    - "personalized explanation in machine learning"
    - "counterfactual visual explanations"
    - "generative image inpainting with contextual attention"
    - "cvae gan fine grained image generation through asymmetric training"
    - "image to image translation with conditional adversarial networks"
    - "generative visual manipulation on the natural image manifold"
    - "k sparse autoencoders"

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)