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: "discovery of shared semantic spaces for multiscene video query and summarization"
  Abstract: The growing rate of public space closed-circuit television (CCTV) installations has generated a need for automated methods for exploiting video surveillance data, including scene understanding, query, behavior annotation, and summarization. For this reason, extensive research has been performed on surveillance scene understanding and analysis. However, most studies have considered single scenes or groups of adjacent scenes. The semantic similarity between different but related scenes (e.g., many different traffic scenes of a similar layout) is not generally exploited to improve any automated surveillance tasks and reduce manual effort. Exploiting commonality and sharing any supervised annotations between different scenes is, however, challenging due to the following reason: some scenes are totally unrelated and thus any information sharing between them would be detrimental, whereas others may share only a subset of common activities and thus information sharing is only useful if it is selective. Moreover, semantically similar activities that should be modeled together and shared across scenes may have quite different pixel-level appearances in each scene. To address these issues, we develop a new framework for distributed multiple-scene global understanding that clusters surveillance scenes by their ability to explain each other’s behaviors and further discovers which subset of activities are shared versus scene specific within each cluster. We show how to use this structured representation of multiple scenes to improve common surveillance tasks, including scene activity understanding, cross-scene query-by-example, behavior classification with reduced supervised labeling requirements, and video summarization. In each case, we demonstrate how our multiscene model improves on a collection of standard single-scene models and a flat model of all scenes.

Connected through hyperedge HE-1 (induced by source paper "i vise interactive video surveillance as an edge service using unsupervised feature queries"):
  with members:
    - "decentralized smart surveillance through microservices platform"
    - "a lightweight blockchain based privacy protection for smart surveillance at the edge"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "a microservice enabled architecture for smart surveillance using blockchain technology"
    - "kerman a hybrid lightweight tracking algorithm to enable smart surveillance as an edge service"
    - "real time index authentication for event oriented surveillance video query using blockchain"
    - "compositional human pose regression"
    - "cascaded pyramid network for multi person pose estimation"

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"
    - "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"
    - "collaborative summarization of topic related videos"
    - "summary transfer exemplar based subset selection for video summarization"

Connected through hyperedge HE-3 (induced by source paper "zero shot crowd behavior recognition"):
  with members:
    - "transductive multi view zero shot learning"
    - "a robust and efficient video representation for action recognition"
    - "transductive multi label zero shot learning"
    - "evaluation of output embeddings for fine grained image classification"
    - "semantic embedding space for zero shot action recognition"
    - "deepid net multi stage and deformable deep convolutional neural networks for object detection"
    - "zero shot learning through cross modal transfer"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-4 (induced by source paper "representing videos based on scene layouts for recognizing agent in place actions"):
  with members:
    - "actor centric relation network"
    - "dynamic temporal pyramid network a closer look at multi scale modeling for activity detection"
    - "rethinking the faster r cnn architecture for temporal action localization"
    - "remotenet efficient relevant motion event detection for large scale home surveillance videos"
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "non local neural networks"
    - "c wsl count guided weakly supervised localization"

Connected through hyperedge HE-5 (induced by source paper "layout induced video representation for recognizing agent in place actions"):
  with members:
    - "actor centric relation network"
    - "learning human object interactions by graph parsing neural networks"
    - "dynamic temporal pyramid network a closer look at multi scale modeling for activity detection"
    - "rethinking the faster r cnn architecture for temporal action localization"
    - "remotenet efficient relevant motion event detection for large scale home surveillance videos"
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "non local neural networks"

Connected through hyperedge HE-6 (induced by source paper "video summarization with attention based encoder decoder networks"):
  with members:
    - "a general framework for edited video and raw video summarization"
    - "learning visual storylines with skipping recurrent neural networks"
    - "summary transfer exemplar based subset selection for video summarization"
    - "effective approaches to attention based neural machine translation"
    - "show attend and tell neural image caption generation with visual attention"

Connected through hyperedge HE-7 (induced by source paper "knowledge transfer for scene specific motion prediction"):
  with members:
    - "forecasting social navigation in crowded complex scenes"
    - "a data driven approach for tag refinement and localization in web videos"
    - "learning to divide and conquer for online multi target tracking"
    - "anticipating visual representations from unlabeled video"
    - "joint inference of groups events and human roles in aerial videos"
    - "anticipating the future by watching unlabeled video"

Connected through hyperedge HE-8 (induced by source paper "determining the best attributes for surveillance video keywords generation"):
  with members:
    - "dynamic concept composition for zero example event detection"
    - "coherent multi sentence video description with variable level of detail"
    - "discovering discriminative cell attributes for hep 2 specimen image classification"

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)