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: "learning from multiple sources for video summarisation"
  Abstract: Many visual surveillance tasks, e.g.video summarisation, is conventionally accomplished through analysing imagerybased features. Relying solely on visual cues for public surveillance video understanding is unreliable, since visual observations obtained from public space CCTV video data are often not sufficiently trustworthy and events of interest can be subtle. On the other hand, non-visual data sources such as weather reports and traffic sensory signals are readily accessible but are not explored jointly to complement visual data for video content analysis and summarisation. In this paper, we present a novel unsupervised framework to learn jointly from both visual and independently-drawn non-visual data sources for discovering meaningful latent structure of surveillance video data. In particular, we investigate ways to cope with discrepant dimension and representation whist associating these heterogeneous data sources, and derive effective mechanism to tolerate with missing and incomplete data from different sources. We show that the proposed multi-source learning framework not only achieves better video content clustering than state-of-the-art methods, but also is capable of accurately inferring missing non-visual semantics from previously unseen videos. In addition, a comprehensive user study is conducted to validate the quality of video summarisation generated using the proposed multi-source model.

Connected through hyperedge HE-1 (induced by source paper "goal driven sequential data abstraction"):
  with members:
    - "learning deep sketch abstraction"
    - "game of sketches deep recurrent models of pictionary style word guessing"
    - "ranking sentences for extractive summarization with reinforcement learning"
    - "dtr gan dilated temporal relational adversarial network for video summarization"
    - "video summarisation by classification with deep reinforcement learning"
    - "discovering visual concept structure with sparse and incomplete tags"
    - "sequential dual deep learning with shape and texture features for sketch recognition"
    - "neural extractive summarization with side information"

Connected through hyperedge HE-2 (induced by source paper "long activity video understanding using functional object oriented network"):
  with members:
    - "functional object oriented network for manipulation learning"
    - "prediction of manipulation actions"
    - "ask me anything free form visual question answering based on knowledge from external sources"
    - "an end to end generative framework for video segmentation and recognition"
    - "p cnn pose based cnn features for action recognition"
    - "structural rnn deep learning on spatio temporal graphs"
    - "tagbook a semantic video representation without supervision for event detection"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-3 (induced by source paper "multi view surveillance video summarization via joint embedding and sparse optimization"):
  with members:
    - "collaborative summarization of topic related videos"
    - "summary transfer exemplar based subset selection for video summarization"
    - "video summarization with long short term memory"
    - "video summarization in a multi view camera network"
    - "correntropy induced l2 graph for robust subspace clustering"
    - "active object localization with deep reinforcement learning"
    - "very deep convolutional networks for large scale image recognition"
    - "learning spatiotemporal features with 3d convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "discovering visual concept structure with sparse and incomplete tags"):
  with members:
    - "learning structured inference neural networks with label relations"
    - "very deep convolutional networks for large scale image recognition"
    - "training convolutional networks with noisy labels"
    - "imagenet large scale visual recognition challenge"
    - "a multi view embedding space for modeling internet images tags and their semantics"
    - "exact matrix completion via convex optimization"
    - "objective criteria for the evaluation of clustering methods"

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)