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 "lipreading using temporal convolutional networks"):
  Abstract: Lip-reading has attracted a lot of research attention lately thanks to advances in deep learning. The current state-of-the-art model for recognition of isolated words in-the-wild consists of a residual network and Bidirectional Gated Recurrent Unit (BGRU) layers. In this work, we address the limitations of this model and we propose changes which further improve its performance. Firstly, the BGRU layers are replaced with Temporal Convolutional Networks (TCN). Secondly, we greatly simplify the training procedure, which allows us to train the model in one single stage. Thirdly, we show that the current state-of-the-art methodology produces models that do not generalize well to variations on the sequence length, and we addresses this issue by proposing a variable-length augmentation. We present results on the largest publicly-available datasets for isolated word recognition in English and Mandarin, LRW and LRW1000, respectively. Our proposed model results in an absolute improvement of 1.2% and 3.2%, respectively, in these datasets which is the new state-of-the-art performance.

Includes members:
  Member "deep audio visual speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "can we read speech beyond the lips rethinking roi selection for deep visual speech recognition")
  Member "learning spatio temporal features with two stream deep 3d cnns for lipreading":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deformation flow based two stream network for lip reading")
  Member "multi grained spatio temporal modeling for lip reading":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "mutual information maximization for effective lip reading")
  Member "large scale visual speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "m 3 t multi modal continuous valence arousal estimation in the wild")
  Member "end to end audiovisual speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "can we read speech beyond the lips rethinking roi selection for deep visual speech recognition")
  Member "lrw 1000 a naturally distributed large scale benchmark for lip reading in the wild":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "can we read speech beyond the lips rethinking roi selection for deep visual speech recognition")
  Member "audio visual speech recognition with a hybrid ctc attention architecture":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "end to end automatic speech recognition integrated with ctc based voice activity detection")
  Member "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "blank language models")

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)