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 "scene labeling using gated recurrent units with explicit long range conditioning"):
  Abstract: Recurrent neural network (RNN), as a powerful contextual dependency modeling framework, has been widely applied to scene labeling problems. However, this work shows that directly applying traditional RNN architectures, which unfolds a 2D lattice grid into a sequence, is not sufficient to model structure dependencies in images due to the "impact vanishing" problem. First, we give an empirical analysis about the "impact vanishing" problem. Then, a new RNN unit named Recurrent Neural Network with explicit long range conditioning (RNN-ELC) is designed to alleviate this problem. A novel neural network architecture is built for scene labeling tasks where one of the variants of the new RNN unit, Gated Recurrent Unit with Explicit Long-range Conditioning (GRU-ELC), is used to model multi scale contextual dependencies in images. We validate the use of GRU-ELC units with state-of-the-art performance on three standard scene labeling datasets. Comprehensive experiments demonstrate that the new GRU-ELC unit benefits scene labeling problem a lot as it can encode longer contextual dependencies in images more effectively than traditional RNN units.

Includes members:
  Member "dense recurrent neural networks for scene labeling":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "video scene parsing with predictive feature learning")
  Member "semantic object parsing with graph lstm":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "hierarchical human parsing with typed part relation reasoning")
  Member "lstm cf unifying context modeling and fusion with lstms for rgb d scene labeling":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "fpconv learning local flattening for point convolution")
  Member "multi level contextual rnns with attention model for scene labeling":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "attention guided chained context aggregation for semantic segmentation")
  Member "learning common and specific features for rgb d semantic segmentation with deconvolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "modality compensation network cross modal adaptation for action recognition")
  Member "multi path feedback recurrent neural network for scene parsing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "accessing higher level representations in sequential transformers with feedback memory")
  Member "combining the best of convolutional layers and recurrent layers a hybrid network for semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "semantic correlation promoted shape variant context for segmentation")
  Member "pixel recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "mast a memory augmented self supervised tracker")

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)