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: "u cam visual explanation using uncertainty based class activation maps"
  Abstract: Understanding and explaining deep learning models is an imperative task. Towards this, we propose a method that obtains gradient-based certainty estimates that also provide visual attention maps. Particularly, we solve for visual question answering task. We incorporate modern probabilistic deep learning methods that we further improve by using the gradients for these estimates. These have two-fold benefits: a) improvement in obtaining the certainty estimates that correlate better with misclassified samples and b) improved attention maps that provide state-of-the-art results in terms of correlation with human attention regions. The improved attention maps result in consistent improvement for various methods for visual question answering. Therefore, the proposed technique can be thought of as a recipe for obtaining improved certainty estimates and explanation for deep learning models. We provide detailed empirical analysis for the visual question answering task on all standard benchmarks and comparison with state of the art methods.

Connected through hyperedge HE-1 (induced by source paper "robust explanations for visual question answering"):
  with members:
    - "explanation vs attention a two player game to obtain attention for vqa"
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "multimodal differential network for visual question generation"
    - "differential attention for visual question answering"
    - "toward controlled generation of text"
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "adversarial learning for neural dialogue generation"
    - "creativity generating diverse questions using variational autoencoders"

Connected through hyperedge HE-2 (induced by source paper "deep bayesian network for visual question generation"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "attending to discriminative certainty for domain adaptation"
    - "multimodal differential network for visual question generation"
    - "evaluating visual conversational agents via cooperative human ai games"
    - "creativity generating diverse questions using variational autoencoders"
    - "what s in a question using visual questions as a form of supervision"
    - "hadamard product for low rank bilinear pooling"
    - "cross modal scene networks"

Connected through hyperedge HE-3 (induced by source paper "uncertainty based class activation maps for visual question answering"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "attending to discriminative certainty for domain adaptation"
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "multimodal differential network for visual question generation"
    - "bilinear attention networks"
    - "learning to count objects in natural images for visual question answering"
    - "differential attention for visual question answering"
    - "learning semantic sentence embeddings using sequential pair wise discriminator"

Connected through hyperedge HE-4 (induced by source paper "probabilistic framework for solving visual dialog"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "attending to discriminative certainty for domain adaptation"
    - "multimodal differential network for visual question generation"
    - "bilinear attention networks"
    - "generating diverse and accurate visual captions by comparative adversarial learning"
    - "learning to count objects in natural images for visual question answering"
    - "differential attention for visual question answering"
    - "visual coreference resolution in visual dialog using neural module networks"

Connected through hyperedge HE-5 (induced by source paper "explanation vs attention a two player game to obtain attention for vqa"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "attention is not explanation"
    - "bilinear attention networks"
    - "learning to count objects in natural images for visual question answering"
    - "differential attention for visual question answering"
    - "rise randomized input sampling for explanation of black box models"
    - "deriving machine attention from human rationales"
    - "mutan multimodal tucker fusion for visual question answering"

Connected through hyperedge HE-6 (induced by source paper "score cam improved visual explanations via score weighted class activation mapping"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "interpretable and fine grained visual explanations for convolutional neural networks"
    - "smooth grad cam an enhanced inference level visualization technique for deep convolutional neural network models"
    - "towards explanation of dnn based prediction with guided feature inversion"
    - "techniques for interpretable machine learning"
    - "local explanation methods for deep neural networks lack sensitivity to parameter values"
    - "explaining image classifiers by counterfactual generation"
    - "rise randomized input sampling for explanation of black box models"

Connected through hyperedge HE-7 (induced by source paper "granular multimodal attention networks for visual dialog"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "multimodal differential network for visual question generation"
    - "differential attention for visual question answering"
    - "visual coreference resolution in visual dialog using neural module networks"
    - "learning semantic sentence embeddings using sequential pair wise discriminator"
    - "rise randomized input sampling for explanation of black box models"
    - "evaluating visual conversational agents via cooperative human ai games"
    - "learning cooperative visual dialog agents with deep reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "dynamic attention networks for task oriented grounding"):
  with members:
    - "probabilistic framework for solving visual dialog"
    - "multimodal differential network for visual question generation"
    - "differential attention for visual question answering"
    - "learning semantic sentence embeddings using sequential pair wise discriminator"
    - "a deep compositional framework for human like language acquisition in virtual environment"
    - "gated attention architectures for task oriented language grounding"
    - "zero shot task generalization with multi task deep reinforcement learning"
    - "creativity generating diverse questions using variational 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.CV(Computer Vision and Pattern Recognition)