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 survey of evaluation methods and measures for interpretable machine learning"
  Abstract: The need for interpretable and accountable intelligent system gets sensible as artificial intelligence plays more role in human life. Explainable artificial intelligence systems can be a solution by self-explaining the reasoning behind the decisions and predictions of the intelligent system. Researchers from different disciplines work together to define, design and evaluate interpretable intelligent systems for the user. Our work supports the different evaluation goals in interpretable machine learning research by a thorough review of evaluation methodologies used in machine-explanation research across the fields of human-computer interaction, visual analytics, and machine learning. We present a 2D categorization of interpretable machine learning evaluation methods and show a mapping between user groups and evaluation measures. Further, we address the essential factors and steps for a right evaluation plan by proposing a nested model for design and evaluation of explainable artificial intelligence systems.

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 "towards hierarchical importance attribution explaining compositional semantics for neural sequence models"):
  with members:
    - "bert rediscovers the classical nlp pipeline"
    - "roberta a robustly optimized bert pretraining approach"
    - "hierarchical interpretations for neural network predictions"
    - "evaluating neural network explanation methods using hybrid documents and morphological agreement"
    - "towards explainable nlp a generative explanation framework for text classification"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "explaining image classifiers by counterfactual generation"

Connected through hyperedge HE-3 (induced by source paper "an explanation method for siamese neural networks"):
  with members:
    - "evaluating explainers via perturbation"
    - "explaining deep neural networks with a polynomial time algorithm for shapley values approximation"
    - "alime autoencoder based approach for local interpretability"
    - "dlime a deterministic local interpretable model agnostic explanations approach for computer aided diagnosis systems"
    - "interpretable counterfactual explanations guided by prototypes"
    - "normlime a new feature importance metric for explaining deep neural networks"
    - "model agnostic contrastive explanations for structured data"
    - "metric learning for novelty and anomaly detection"

Connected through hyperedge HE-4 (induced by source paper "recovering localized adversarial attacks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "adversarial attacks hidden in plain sight"
    - "explainable artificial intelligence understanding visualizing and interpreting deep learning models"
    - "foolbox v0 8 0 a python toolbox to benchmark the robustness of machine learning models"
    - "why should i trust you explaining the predictions of any classifier"
    - "adversarial machine learning at scale"
    - "deep learning in neural networks"
    - "rethinking the inception architecture for computer vision"

Connected through hyperedge HE-5 (induced by source paper "towards a rigorous evaluation of xai methods on time series"):
  with members:
    - "explaining deep classification of time series data with learned prototypes"
    - "evaluating recurrent neural network explanations"
    - "yolov3 an incremental improvement"
    - "explainable artificial intelligence understanding visualizing and interpreting deep learning models"
    - "a unified approach to interpreting model predictions"
    - "learning important features through propagating activation differences"
    - "an empirical evaluation of deep learning on highway driving"
    - "visualizing and understanding convolutional networks"

Connected through hyperedge HE-6 (induced by source paper "open issues in combating fake news interpretability as an opportunity"):
  with members:
    - "combating fake news a survey on identification and mitigation techniques"
    - "fakenewsnet a data repository with news content social context and dynamic information for studying fake news on social media"
    - "declare debunking fake news and false claims using evidence aware deep learning"
    - "fake news a survey of research detection methods and opportunities"
    - "beyond pixels image provenance analysis leveraging metadata"
    - "mitigating confirmation bias on twitter by recommending opposing views"
    - "beyond news contents the role of social context for fake news detection"
    - "some like it hoax automated fake news detection in social networks"

Connected through hyperedge HE-7 (induced by source paper "physics enhanced artificial intelligence"):
  with members:
    - "identifying and correcting label bias in machine learning"
    - "explanation in artificial intelligence insights from the social sciences"
    - "physics informed deep learning part ii data driven discovery of nonlinear partial differential equations"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "surpassing human level face verification performance on lfw with gaussianface"

Connected through hyperedge HE-8 (induced by source paper "consistent feature construction with constrained genetic programming for experimental physics"):
  with members:
    - "explaining explanations an overview of interpretability of machine learning"
    - "feature engineering for predictive modeling using reinforcement learning"
    - "deep learning of representations looking forward"
    - "avoiding the bloat with stochastic grammar based genetic programming"

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.HC(Human-Computer Interaction)