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 "memory attended recurrent network for video captioning"):
  Abstract: Typical techniques for video captioning follow the encoder-decoder framework, which can only focus on one source video being processed. A potential disadvantage of such design is that it cannot capture the multiple visual context information of a word appearing in more than one relevant videos in training data. To tackle this limitation, we propose the Memory-Attended Recurrent Network (MARN) for video captioning, in which a memory structure is designed to explore the full-spectrum correspondence between a word and its various similar visual contexts across videos in training data. Thus, our model is able to achieve a more comprehensive understanding for each word and yield higher captioning quality. Furthermore, the built memory structure enables our method to model the compatibility between adjacent words explicitly instead of asking the model to learn implicitly, as most existing models do. Extensive validation on two real-word datasets demonstrates that our MARN consistently outperforms state-of-the-art methods.

Includes members:
  Member "less is more picking informative frames for video captioning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "spatio temporal ranked attention networks for video captioning")
  Member "bidirectional attentive fusion with context gating for dense video captioning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "ha ccn hierarchical attention based crowd counting network")
  Member "automatic stance detection using end to end memory networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "joint inference on truth rumor and their sources in social networks")
  Member "reconstruction network for video captioning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "object relational graph with teacher recommended learning for video captioning")
  Member "recurrent fusion network for image captioning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deconfounded image captioning a causal retrospect")
  Member "end to end video captioning with multitask reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "watch it twice video captioning with a refocused video encoder")
  Member "a closer look at spatiotemporal convolutions for action recognition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "probabilistic future prediction for video scene understanding")
  Member "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning spatiotemporal features via video and text pair discrimination")

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.CV(Computer Vision and Pattern Recognition)