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: "excitation backprop for rnns"
  Abstract: Deep models are state-of-the-art for many vision tasks including video action recognition and video captioning. Models are trained to caption or classify activity in videos, but little is known about the evidence used to make such decisions. Grounding decisions made by deep networks has been studied in spatial visual content, giving more insight into model predictions for images. However, such studies are relatively lacking for models of spatiotemporal visual content - videos. In this work, we devise a formulation that simultaneously grounds evidence in space and time, in a single pass, using top-down saliency. We visualize the spatiotemporal cues that contribute to a deep model's classification/captioning output using the model's internal representation. Based on these spatiotemporal cues, we are able to localize segments within a video that correspond with a specific action, or phrase from a caption, without explicitly optimizing/training for these tasks.

Connected through hyperedge HE-1 (induced by source paper "explainable deep classification models for domain generalization"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "addressing model vulnerability to distributional shifts over image transformation sets"
    - "episodic training for domain generalization"
    - "domain generalization by solving jigsaw puzzles"
    - "weakly supervised instance segmentation using class peak response"
    - "excitation dropout encouraging plasticity in deep neural networks"
    - "moment matching for multi source domain adaptation"
    - "adversarial complementary learning for weakly supervised object localization"

Connected through hyperedge HE-2 (induced by source paper "explainable machine learning in deployment"):
  with members:
    - "the what if tool interactive probing of machine learning models"
    - "understanding impacts of high order loss approximations and features in deep learning interpretation"
    - "certifai counterfactual explanations for robustness transparency interpretability and fairness of artificial intelligence models"
    - "transparency motivations and challenges"
    - "automatic model monitoring for data streams"
    - "why should you trust my explanation understanding uncertainty in lime explanations"
    - "neural network attributions a causal perspective"
    - "explaining explanations in ai"

Connected through hyperedge HE-3 (induced by source paper "attentive action and context factorization"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "video inpainting by jointly learning temporal structure and spatial details"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "adversarial variational bayes unifying variational autoencoders and generative adversarial networks"
    - "non local neural networks"
    - "attention is all you need"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "unsupervised learning of disentangled representations from video"

Connected through hyperedge HE-4 (induced by source paper "saliency tubes visual explanations for spatio temporal convolutions"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "scaling egocentric vision the epic kitchens dataset"
    - "multi fiber networks for video recognition"
    - "methods for interpreting and understanding deep neural networks"
    - "analyzing human human interactions a survey"
    - "network dissection quantifying interpretability of deep visual representations"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "interpreting cnn knowledge via an explanatory graph"

Connected through hyperedge HE-5 (induced by source paper "simple vs complex temporal recurrences for video saliency prediction"):
  with members:
    - "deep visual attention prediction"
    - "revisiting video saliency a large scale benchmark and a new model"
    - "deepvs a deep learning based video saliency prediction approach"
    - "youtube vos sequence to sequence video object segmentation"
    - "salgan visual saliency prediction with generative adversarial networks"
    - "temporal activity detection in untrimmed videos with recurrent neural networks"
    - "spatio temporal saliency networks for dynamic saliency prediction"
    - "shallow and deep convolutional networks for saliency prediction"

Connected through hyperedge HE-6 (induced by source paper "interpreting video features a comparison of 3d convolutional networks and convolutional lstm networks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "video time properties encoders and evaluation"
    - "what have we learned from deep representations for action recognition"
    - "methods for interpreting and understanding deep neural networks"
    - "sanity checks for saliency maps"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "interpretable explanations of black boxes by meaningful perturbation"
    - "temporal relational reasoning in videos"

Connected through hyperedge HE-7 (induced by source paper "deepgru deep gesture recognition utility"):
  with members:
    - "exploiting recurrent neural networks and leap motion controller for the recognition of sign language and semaphoric hand gestures"
    - "2d 3d pose estimation and action recognition using multitask deep learning"
    - "glimpse clouds human activity recognition from unstructured feature points"
    - "cnn based action recognition and supervised domain adaptation on 3d body skeletons via kernel feature maps"
    - "non linear temporal subspace representations for activity recognition"
    - "sts classification with dual stream cnn"
    - "skeleton based human action recognition with global context aware attention lstm networks"
    - "motion feature augmented recurrent neural network for skeleton based dynamic hand gesture recognition"

Connected through hyperedge HE-8 (induced by source paper "grad cam improved visual explanations for deep convolutional networks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "tell me where to look guided attention inference network"
    - "contextual explanation networks"
    - "interpretable learning for self driving cars by visualizing causal attention"
    - "visual explanations for convolutional neural networks via input resampling"
    - "learning important features through propagating activation differences"
    - "paying more attention to attention improving the performance of convolutional neural networks via attention transfer"
    - "deep model compression distilling knowledge from noisy teachers"

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)