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: "collaborative summarization of topic related videos"
  Abstract: Large collections of videos are grouped into clusters by a topic keyword, such as Eiffel Tower or Surfing, with many important visual concepts repeating across them. Such a topically close set of videos have mutual influence on each other, which could be used to summarize one of them by exploiting information from others in the set. We build on this intuition to develop a novel approach to extract a summary that simultaneously captures both important particularities arising in the given video, as well as, generalities identified from the set of videos. The topic-related videos provide visual context to identify the important parts of the video being summarized. We achieve this by developing a collaborative sparse optimization method which can be efficiently solved by a half-quadratic minimization algorithm. Our work builds upon the idea of collaborative techniques from information retrieval and natural language processing, which typically use the attributes of other similar objects to predict the attribute of a given object. Experiments on two challenging and diverse datasets well demonstrate the efficacy of our approach over state-of-the-art methods.

Connected through hyperedge HE-1 (induced by source paper "meta learning for task driven video summarization"):
  with members:
    - "hierarchical recurrent neural network for video summarization"
    - "a general framework for edited video and raw video summarization"
    - "patch alignment manifold matting"
    - "deep reinforcement learning for unsupervised video summarization with diversity representativeness reward"
    - "video summarization with attention based encoder decoder networks"
    - "meta sgd learning to learn quickly for few shot learning"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "learning to learn by gradient descent by gradient descent"

Connected through hyperedge HE-2 (induced by source paper "video skimming taxonomy and comprehensive survey"):
  with members:
    - "hierarchical recurrent neural network for video summarization"
    - "a general framework for edited video and raw video summarization"
    - "phd gifs personalized highlight detection for automatic gif creation"
    - "discovery of shared semantic spaces for multiscene video query and summarization"
    - "summarizing first person videos from third persons points of views"
    - "video summarization with attention based encoder decoder networks"
    - "query focused video summarization dataset evaluation and a memory network based approach"
    - "summary transfer exemplar based subset selection for video summarization"

Connected through hyperedge HE-3 (induced by source paper "less is more learning highlight detection from video duration"):
  with members:
    - "attention based deep multiple instance learning"
    - "exploring the limits of weakly supervised pretraining"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "learning from noisy labels with distillation"
    - "video summarization with long short term memory"
    - "youtube 8m a large scale video classification benchmark"
    - "video2gif automatic generation of animated gifs from video"

Connected through hyperedge HE-4 (induced by source paper "video summarization through human detection on a social robot"):
  with members:
    - "yolov3 an incremental improvement"
    - "deep reinforcement learning for unsupervised video summarization with diversity representativeness reward"
    - "a pose sensitive embedding for person re identification with expanded cross neighborhood re ranking"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "hollywood in homes crowdsourcing data collection for activity understanding"
    - "video summarization with long short term memory"
    - "title generation for user generated videos"
    - "video2gif automatic generation of animated gifs from video"

Connected through hyperedge HE-5 (induced by source paper "dilated temporal relational adversarial network for generic video summarization"):
  with members:
    - "viewpoint aware video summarization"
    - "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"
    - "rethinking atrous convolution for semantic image segmentation"
    - "disentangling factors of variation in deep representations using adversarial training"

Connected through hyperedge HE-6 (induced by source paper "contemplating visual emotions understanding and overcoming dataset bias"):
  with members:
    - "heterogeneous knowledge transfer in video emotion recognition attribution and summarization"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "building emotional machines recognizing image emotions through deep neural networks"
    - "curriculum domain adaptation for semantic segmentation of urban scenes"
    - "webvision database visual learning and understanding from web data"
    - "automatic understanding of image and video advertisements"
    - "multi task curriculum transfer deep learning of clothing attributes"
    - "building a large scale dataset for image emotion recognition the fine print and the benchmark"

Connected through hyperedge HE-7 (induced by source paper "video summarization using fully convolutional sequence networks"):
  with members:
    - "a general framework for edited video and raw video summarization"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "query focused video summarization dataset evaluation and a memory network based approach"
    - "convolutional sequence to sequence learning"
    - "temporal convolutional networks for action segmentation and detection"
    - "energy based generative adversarial network"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "summary transfer exemplar based subset selection for video summarization"

Connected through hyperedge HE-8 (induced by source paper "ffnet video fast forwarding via reinforcement learning"):
  with members:
    - "egosampling wide view hyperlapse from egocentric videos"
    - "deep reinforcement learning based image captioning with embedding reward"
    - "towards semantic fast forward and stabilized egocentric videos"
    - "poseagent budget constrained 6d object pose estimation via reinforcement learning"
    - "learning visual storylines with skipping recurrent neural networks"
    - "summary transfer exemplar based subset selection for video summarization"
    - "video summarization with long short term memory"
    - "leaving some stones unturned dynamic feature prioritization for activity detection in streaming video"

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)