human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "towards extracting highlights from recorded live videos an implicit crowdsourcing approach"):
  Abstract: Live streaming platforms need to store a lot of recorded live videos on a daily basis. An important problem is how to automatically extract highlights (i.e., attractive short video clips) from these massive, long recorded live videos. One approach is to directly apply a highlight extraction algorithm to video content. However, algorithmic approaches are either domain-specific, which require experts to spend a long time to design, or resource-intensive, which require a lot of training data and/or computing resources. In this paper, we propose Lightor, a novel implicit crowdsourcing approach to overcome these limitations. The key insight is to collect users' natural interactions with a live streaming platform, and then leverage them to detect highlights. Lightor consists of two major components. Highlight Initializer collects time-stamped chat messages from a live video and then uses them to predict approximate highlight positions. Highlight Extractor keeps track of how users interact with these approximate highlight positions and then refines these positions iteratively. We find that the collected user chat and interaction data are very noisy, and propose effective techniques to deal with noise. Lightor can be easily deployed into existing live streaming platforms, or be implemented as a web browser extension. We recruit hundreds of users from Amazon Mechanical Turk, and evaluate the performance of Lightor using two popular games in Twitch. The results show that Lightor can achieve high extraction precision with a small set of training data and low computing resources.

Includes members:
  Member "deeplens towards a visual data management system":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "querying zero streaming cameras")
  Member "video highlight prediction using audience chat reactions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "e sports talent scouting based on multimodal twitch stream data")
  Member "video highlights detection and summarization with lag calibration based on concept emotion mapping of crowd sourced time sync comments":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "interactive variance attention based online spoiler detection for time sync comments")
  Member "real time video highlights for yahoo esports":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "rethinking the evaluation of video summaries")
  Member "video summarization using deep semantic features":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "convolutional hierarchical attention network for query focused video summarization")
  Member "unsupervised extraction of video highlights via robust recurrent auto encoders":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "summarynet a multi stage deep learning model for automatic video summarisation")
  Member "your click decides your fate leveraging clickstream patterns in mooc videos to infer students information processing and attrition behavior":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "dropout prediction over weeks in moocs via interpretable multi layer representation learning")
  Member "generating sequences with recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")

Question: Please tell me which class the source paper 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.DB(Databases)