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: "viewpoint aware video summarization"
  Abstract: This paper introduces a novel variant of video summarization, namely building a summary that depends on the particular aspect of a video the viewer focuses on. We refer to this as $\textit{viewpoint}$. To infer what the desired $\textit{viewpoint}$ may be, we assume that several other videos are available, especially groups of videos, e.g., as folders on a person's phone or laptop. The semantic similarity between videos in a group vs. the dissimilarity between groups is used to produce $\textit{viewpoint}$-specific summaries. For considering similarity as well as avoiding redundancy, output summary should be (A) diverse, (B) representative of videos in the same group, and (C) discriminative against videos in the different groups. To satisfy these requirements (A)-(C) simultaneously, we proposed a novel video summarization method from multiple groups of videos. Inspired by Fisher's discriminant criteria, it selects summary by optimizing the combination of three terms (a) inner-summary, (b) inner-group, and (c) between-group variances defined on the feature representation of summary, which can simply represent (A)-(C). Moreover, we developed a novel dataset to investigate how well the generated summary reflects the underlying $\textit{viewpoint}$. Quantitative and qualitative experiments conducted on the dataset demonstrate the effectiveness of proposed method.

Connected through hyperedge HE-1 (induced by source paper "convolutional hierarchical attention network for query focused video summarization"):
  with members:
    - "query conditioned three player adversarial network for video summarization"
    - "video summarization using fully convolutional sequence networks"
    - "query adaptive video summarization via quality aware relevance estimation"
    - "deep reinforcement learning for unsupervised video summarization with diversity representativeness reward"
    - "query focused video summarization dataset evaluation and a memory network based approach"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "query focused extractive video summarization"
    - "video summarization using deep semantic features"

Connected through hyperedge HE-2 (induced by source paper "dilated temporal relational adversarial network for generic video summarization"):
  with members:
    - "how local is the local diversity reinforcing sequential determinantal point processes with dynamic ground sets for supervised 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-3 (induced by source paper "multimodal explanations by predicting counterfactuality in videos"):
  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"
    - "learning to explain an information theoretic perspective on model interpretation"
    - "learning to explain with complemental examples"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "counterfactual explanations without opening the black box automated decisions and the gdpr"
    - "right for the right reasons training differentiable models by constraining their explanations"

Connected through hyperedge HE-4 (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"
    - "multimodal explanations by predicting counterfactuality in videos"
    - "teaching categories to human learners with visual explanations"
    - "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"

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)