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: "teaching categories to human learners with visual explanations"
  Abstract: We study the problem of computer-assisted teaching with explanations. Conventional approaches for machine teaching typically only provide feedback at the instance level e.g., the category or label of the instance. However, it is intuitive that clear explanations from a knowledgeable teacher can significantly improve a student's ability to learn a new concept. To address these existing limitations, we propose a teaching framework that provides interpretable explanations as feedback and models how the learner incorporates this additional information. In the case of images, we show that we can automatically generate explanations that highlight the parts of the image that are responsible for the class label. Experiments on human learners illustrate that, on average, participants achieve better test set performance on challenging categorization tasks when taught with our interpretable approach compared to existing methods.

Connected through hyperedge HE-1 (induced by source paper "adaptive teaching of temporal logic formulas to learners with preferences"):
  with members:
    - "transfer of temporal logic formulas in reinforcement learning"
    - "understanding the role of adaptivity in machine teaching the case of version space learners"
    - "information guided temporal logic inference with prior knowledge"
    - "mining parametric temporal logic properties in model based design for cyber physical systems"
    - "census signal temporal logic inference for multiagent group behavior analysis"
    - "learning linear temporal properties"
    - "machine teaching for inverse reinforcement learning algorithms and applications"
    - "learning task specifications from demonstrations"

Connected through hyperedge HE-2 (induced by source paper "counterfactual visual explanations"):
  with members:
    - "grounding visual explanations"
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "embedding deep networks into visual explanations"
    - "explanations based on the missing towards contrastive explanations with pertinent negatives"
    - "explaining image classifiers by counterfactual generation"
    - "sanity checks for saliency maps"
    - "visualizing deep neural network decisions prediction difference analysis"
    - "interpretable explanations of black boxes by meaningful perturbation"

Connected through hyperedge HE-3 (induced by source paper "learner aware teaching inverse reinforcement learning with preferences and constraints"):
  with members:
    - "interactive teaching algorithms for inverse reinforcement learning"
    - "risk aware active inverse reinforcement learning"
    - "an algorithmic perspective on imitation learning"
    - "understanding the role of adaptivity in machine teaching the case of version space learners"
    - "teaching inverse reinforcement learners via features and demonstrations"
    - "an overview of machine teaching"
    - "machine teaching for inverse reinforcement learning algorithms and applications"
    - "repeated inverse reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "on human predictions with explanations and predictions of machine learning models a case study on deception detection"):
  with members:
    - "what can ai do for me evaluating machine learning interpretations in cooperative play"
    - "do explanations make vqa models more predictable to a human"
    - "a unified approach to interpreting model predictions"
    - "algorithmic decision making and the cost of fairness"
    - "improving human machine cooperative visual search with soft highlighting"
    - "equality of opportunity in supervised learning"
    - "inherent trade offs in the fair determination of risk scores"
    - "rationalizing neural predictions"

Connected through hyperedge HE-5 (induced by source paper "few shot learning with per sample rich supervision"):
  with members:
    - "adaptive confidence smoothing for generalized zero shot learning"
    - "link the head to the beak zero shot learning from noisy text description at part precision"
    - "prototypical networks for few shot learning"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "zero shot learning the good the bad and the ugly"
    - "matching networks for one shot learning"
    - "low shot visual object recognition"
    - "low shot visual recognition by shrinking and hallucinating features"

Connected through hyperedge HE-6 (induced by source paper "large scale fine grained categorization and domain specific transfer learning"):
  with members:
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "generalized orderless pooling performs implicit salient matching"
    - "the devil is in the tails fine grained classification in the wild"
    - "learning transferable architectures for scalable image recognition"
    - "deep layer aggregation"
    - "fine grained recognition in the wild a multi task domain adaptation approach"
    - "squeeze and excitation networks"
    - "improved bilinear pooling with cnns"

Connected through hyperedge HE-7 (induced by source paper "learning to explain with complemental examples"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "grounding visual explanations"
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "viewpoint aware video summarization"
    - "multimodal explanations by predicting counterfactuality in videos"
    - "learning to explain an information theoretic perspective on model interpretation"
    - "interpretable explanations of black boxes by meaningful perturbation"
    - "a joint speaker listener reinforcer model for referring expressions"

Connected through hyperedge HE-8 (induced by source paper "teaching inverse reinforcement learners via features and demonstrations"):
  with members:
    - "understanding the role of adaptivity in machine teaching the case of version space learners"
    - "teaching multiple concepts to forgetful learners"
    - "iterative classroom teaching"
    - "an overview of machine teaching"
    - "machine teaching for inverse reinforcement learning algorithms and applications"
    - "third person imitation learning"
    - "time contrastive networks self supervised learning from video"
    - "near optimally teaching the crowd to classify"

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.CV(Computer Vision and Pattern Recognition)