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 "delving deeper into convolutional networks for learning video representations"):
  Abstract: We propose an approach to learn spatio-temporal features in videos from intermediate visual representations we call "percepts" using Gated-Recurrent-Unit Recurrent Networks (GRUs).Our method relies on percepts that are extracted from all level of a deep convolutional network trained on the large ImageNet dataset. While high-level percepts contain highly discriminative information, they tend to have a low-spatial resolution. Low-level percepts, on the other hand, preserve a higher spatial resolution from which we can model finer motion patterns. Using low-level percepts can leads to high-dimensionality video representations. To mitigate this effect and control the model number of parameters, we introduce a variant of the GRU model that leverages the convolution operations to enforce sparse connectivity of the model units and share parameters across the input spatial locations. #R##N#We empirically validate our approach on both Human Action Recognition and Video Captioning tasks. In particular, we achieve results equivalent to state-of-art on the YouTube2Text dataset using a simpler text-decoder model and without extra 3D CNN features.

Includes members:
  Member "unsupervised learning of video representations using lstms":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "transformation based adversarial video prediction on large scale data")
  Member "beyond short snippets deep networks for video classification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning spatiotemporal features via video and text pair discrimination")
  Member "hierarchical recurrent neural encoder for video representation with application to captioning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "object relational graph with teacher recommended learning for video captioning")
  Member "convolutional lstm network a machine learning approach for precipitation nowcasting":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "transformation based adversarial video prediction on large scale data")
  Member "towards good practices for very deep two stream convnets":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "ttpp temporal transformer with progressive prediction for efficient action anticipation")
  Member "action recognition with trajectory pooled deep convolutional descriptors":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "panda a gigapixel level human centric video dataset")
  Member "adam a method for stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "going deeper with convolutions":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "discrimination aware network pruning for deep model compression")

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)