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 "refined gate a simple and effective gating mechanism for recurrent units"):
  Abstract: Recurrent neural network (RNN) has been widely studied in sequence learning tasks, while the mainstream models (e.g., LSTM and GRU) rely on the gating mechanism (in control of how information flows between hidden states). However, the vanilla gates in RNN (e.g. the input gate in LSTM) suffer from the problem of gate undertraining mainly due to the saturating activation functions, which may result in failures of learning gating roles and thus the weak performance. In this paper, we propose a new gating mechanism within general gated recurrent neural networks to handle this issue. Specifically, the proposed gates directly short connect the extracted input features to the outputs of vanilla gates, denoted as refined gates. The refining mechanism allows enhancing gradient back-propagation as well as extending the gating activation scope, which, although simple, can guide RNN to reach possibly deeper minima. We verify the proposed gating mechanism on three popular types of gated RNNs including LSTM, GRU and MGU. Extensive experiments on 3 synthetic tasks, 3 language modeling tasks and 5 scene text recognition benchmarks demonstrate the effectiveness of our method.

Includes members:
  Member "towards non saturating recurrent units for modelling long term dependencies":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a spike in performance training hybrid spiking neural networks with quantized activation functions")
  Member "what is wrong with scene text recognition model comparisons dataset and model analysis":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "efficient backbone search for scene text recognition")
  Member "non normal recurrent neural network nnrnn learning long time dependencies while improving expressivity with transient dynamics":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "eigenvalue normalized recurrent neural networks for short term memory")
  Member "independently recurrent neural network indrnn building a longer and deeper rnn":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a survey on 3d skeleton based action recognition using learning method")
  Member "simple recurrent units for highly parallelizable recurrence":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "mhsan multi head self attention network for visual semantic embedding")
  Member "lstm a search space odyssey":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "dl based csi feedback and cooperative recovery in massive mimo")
  Member "a flexible approach to automated rnn architecture generation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "genetic neural architecture search for automatic assessment of human sperm images")
  Member "residual lstm design of a deep recurrent architecture for distant speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "steerage synthesis of neural networks using architecture search and grow and prune methods")

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)