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: "machine teaching a new paradigm for building machine learning systems"
  Abstract: The current processes for building machine learning systems require practitioners with deep knowledge of machine learning. This significantly limits the number of machine learning systems that can be created and has led to a mismatch between the demand for machine learning systems and the ability for organizations to build them. We believe that in order to meet this growing demand for machine learning systems we must significantly increase the number of individuals that can teach machines. We postulate that we can achieve this goal by making the process of teaching machines easy, fast and above all, universally accessible. #R##N#While machine learning focuses on creating new algorithms and improving the accuracy of "learners", the machine teaching discipline focuses on the efficacy of the "teachers". Machine teaching as a discipline is a paradigm shift that follows and extends principles of software engineering and programming languages. We put a strong emphasis on the teacher and the teacher's interaction with data, as well as crucial components such as techniques and design principles of interaction and visualization. #R##N#In this paper, we present our position regarding the discipline of machine teaching and articulate fundamental machine teaching principles. We also describe how, by decoupling knowledge about machine learning algorithms from the process of teaching, we can accelerate innovation and empower millions of new uses for machine learning models.

Connected through hyperedge HE-1 (induced by source paper "explainer a visual analytics framework for interactive and explainable machine learning"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "speculative execution for guided visual analytics"
    - "gan lab understanding complex deep generative models using interactive visual experimentation"
    - "human centered tools for coping with imperfect algorithms during medical decision making"
    - "retainvis visual analytics with interpretable and interactive recurrent neural networks on electronic medical records"
    - "manifold a model agnostic framework for interpretation and diagnosis of machine learning models"
    - "rulematrix visualizing and understanding classifiers with rules"
    - "visual analytics in deep learning an interrogative survey for the next frontiers"

Connected through hyperedge HE-2 (induced by source paper "undefined behavior guarantee by switching to model based controller according to the embedded dynamics in recurrent neural network"):
  with members:
    - "online self supervised learning for object picking detecting optimum grasping position using a metric learning approach"
    - "control barrier functions theory and applications"
    - "grasp2vec learning object representations from self supervised grasping"
    - "residual reinforcement learning for robot control"
    - "variational discriminator bottleneck improving imitation learning inverse rl and gans by constraining information flow"
    - "motion switching with sensory and instruction signals by designing dynamical systems using deep neural network"
    - "learning hand eye coordination for robotic grasping with deep learning and large scale data collection"
    - "generative adversarial imitation learning"

Connected through hyperedge HE-3 (induced by source paper "towards crisp ml q a machine learning process model with quality assurance methodology"):
  with members:
    - "quantifying interpretability and trust in machine learning systems"
    - "machine learning testing survey landscapes and horizons"
    - "deep visual domain adaptation a survey"
    - "deep reinforcement learning"
    - "on the state of the art of evaluation in neural language models"
    - "what is relevant in a text document an interpretable machine learning approach"
    - "deeptest automated testing of deep neural network driven autonomous cars"
    - "why should i trust you explaining the predictions of any classifier"

Connected through hyperedge HE-4 (induced by source paper "crowdsourcing the perception of machine teaching"):
  with members:
    - "diversity in machine learning"
    - "the challenge of crafting intelligible intelligence"
    - "trick me if you can human in the loop generation of adversarial examples for question answering"
    - "meta transfer learning for few shot learning"
    - "an overview of machine teaching"
    - "understanding hand object manipulation by modeling the contextual relationship between actions grasp types and object attributes"
    - "a data driven analysis of workers earnings on amazon mechanical turk"
    - "becoming the expert interactive multi class machine teaching"

Connected through hyperedge HE-5 (induced by source paper "explanation based tuning of opaque machine learners with application to paper recommendation"):
  with members:
    - "a scalable hybrid research paper recommender system for microsoft academic"
    - "scibert a pretrained language model for scientific text"
    - "the challenge of crafting intelligible intelligence"
    - "explainable recommendation a survey and new perspectives"
    - "construction of the literature graph in semantic scholar"
    - "interpretable explorable approximations of black box models"
    - "a unified approach to interpreting model predictions"
    - "why should i trust you explaining the predictions of any classifier"

Connected through hyperedge HE-6 (induced by source paper "explainable active learning xal an empirical study of how local explanations impact annotator experience"):
  with members:
    - "one explanation does not fit all a toolkit and taxonomy of ai explainability techniques"
    - "on human predictions with explanations and predictions of machine learning models a case study on deception detection"
    - "stopping active learning based on predicted change of f measure for text classification"
    - "explaining models an empirical study of how explanations impact fairness judgment"
    - "manipulating and measuring model interpretability"
    - "impact of batch size on stopping active learning for text classification"
    - "a unified approach to interpreting model predictions"
    - "why should i trust you explaining the predictions of any classifier"

Connected through hyperedge HE-7 (induced by source paper "learning the designer s preferences to drive evolution"):
  with members:
    - "procedural content generation through quality diversity"
    - "pitako recommending game design elements in cicero"
    - "empowering quality diversity in dungeon design with interactive constrained map elites"
    - "talakat bullet hell generation through constrained map elites"
    - "co creative level design via machine learning"
    - "procedural content generation via machine learning pcgml"
    - "illuminating search spaces by mapping elites"

Connected through hyperedge HE-8 (induced by source paper "assessment of faster r cnn in man machine collaborative search"):
  with members:
    - "adversarial attacks against medical deep learning systems"
    - "adversarial examples that fool both computer vision and time limited humans"
    - "attention allocation aid for visual search"
    - "evaluating visual conversational agents via cooperative human ai games"
    - "chexnet radiologist level pneumonia detection on chest x rays with deep learning"
    - "improving small object proposals for company logo detection"
    - "a survey on deep learning in medical image analysis"
    - "comparing deep neural networks against humans object recognition when the signal gets weaker"

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)