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: "how local is the local diversity reinforcing sequential determinantal point processes with dynamic ground sets for supervised video summarization"
  Abstract: The large volume of video content and high viewing frequency demand automatic video summarization algorithms, of which a key property is the capability of modeling diversity. If videos are lengthy like hours-long egocentric videos, it is necessary to track the temporal structures of the videos and enforce local diversity. The local diversity refers to that the shots selected from a short time duration are diverse but visually similar shots are allowed to co-exist in the summary if they appear far apart in the video. In this paper, we propose a novel probabilistic model, built upon SeqDPP, to dynamically control the time span of a video segment upon which the local diversity is imposed. In particular, we enable SeqDPP to learn to automatically infer how local the local diversity is supposed to be from the input video. The resulting model is extremely involved to train by the hallmark maximum likelihood estimation (MLE), which further suffers from the exposure bias and non-differentiable evaluation metrics. To tackle these problems, we instead devise a reinforcement learning algorithm for training the proposed model. Extensive experiments verify the advantages of our model and the new learning algorithm over MLE-based methods.

Connected through hyperedge HE-1 (induced by source paper "nattack learning the distributions of adversarial examples for an improved black box attack on deep neural networks"):
  with members:
    - "a direct approach to robust deep learning using adversarial networks"
    - "interpreting adversarial examples by activation promotion and suppression"
    - "adv bnn improved adversarial defense through robust bayesian neural network"
    - "stochastic activation pruning for robust adversarial defense"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "lipschitz margin training scalable certification of perturbation invariance for deep neural networks"
    - "on the robustness of the cvpr 2018 white box adversarial example defenses"
    - "deflecting adversarial attacks with pixel deflection"

Connected through hyperedge HE-2 (induced by source paper "depthwise convolution is all you need for learning multiple visual domains"):
  with members:
    - "taskonomy disentangling task transfer learning"
    - "a survey on methods and theories of quantized neural networks"
    - "piggyback adapting a single network to multiple tasks by learning to mask weights"
    - "network dissection quantifying interpretability of deep visual representations"
    - "transitive invariance for self supervised visual representation learning"
    - "universal representations the missing link between faces text planktons and cat breeds"
    - "vqs linking segmentations to questions and answers for supervised attention in vqa and question focused semantic segmentation"
    - "incremental learning through deep adaptation"

Connected through hyperedge HE-3 (induced by source paper "joint modeling of dense and incomplete trajectories for citywide traffic volume inference"):
  with members:
    - "revisiting spatial temporal similarity a deep learning framework for traffic prediction"
    - "deep multi view spatial temporal network for taxi demand prediction"
    - "an attention based collaboration framework for multi view network representation learning"
    - "xgboost a scalable tree boosting system"
    - "adam a method for stochastic optimization"
    - "inferring uncertain trajectories from partial observations"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-4 (induced by source paper "dilated temporal relational adversarial network for generic video summarization"):
  with members:
    - "viewpoint aware video summarization"
    - "dual motion gan for future flow embedded video prediction"
    - "deep reinforcement learning for unsupervised video summarization with diversity representativeness reward"
    - "video summarization with attention based encoder decoder networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "collaborative summarization of topic related videos"
    - "rethinking atrous convolution for semantic image segmentation"
    - "disentangling factors of variation in deep representations using adversarial training"

Connected through hyperedge HE-5 (induced by source paper "multi stream dynamic video summarization"):
  with members:
    - "hierarchical recurrent neural network for video summarization"
    - "video summarization via actionness ranking"
    - "improving sequential determinantal point processes for supervised video summarization"
    - "diversity aware multi video summarization"
    - "identifying first person camera wearers in third person videos"
    - "video summarization with attention based encoder decoder networks"
    - "query focused video summarization dataset evaluation and a memory network based approach"
    - "multi view surveillance video summarization via joint embedding and sparse optimization"

Connected through hyperedge HE-6 (induced by source paper "video summarization by learning from unpaired data"):
  with members:
    - "hierarchical recurrent neural network for video summarization"
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "query conditioned three player adversarial network for video summarization"
    - "improving sequential determinantal point processes for supervised video summarization"
    - "video summarization using fully convolutional sequence networks"
    - "video summarisation by classification with deep reinforcement learning"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "collaborative summarization of topic related videos"

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)