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 "combining residual networks with lstms for lipreading"):
  Abstract: We propose an end-to-end deep learning architecture for word-level visual speech recognition. The system is a combination of spatiotemporal convolutional, residual and bidirectional Long Short-Term Memory networks. We train and evaluate it on the Lipreading In-The-Wild benchmark, a challenging database of 500-size target-words consisting of 1.28sec video excerpts from BBC TV broadcasts. The proposed network attains word accuracy equal to 83.0, yielding 6.8 absolute improvement over the current state-of-the-art, without using information about word boundaries during training or testing.

Includes members:
  Member "direct acoustics to word models for english conversational speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "acoustic to word models with conversational context information")
  Member "lipnet end to end sentence level lipreading":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "can we read speech beyond the lips rethinking roi selection for deep visual speech recognition")
  Member "decoding visemes improving machine lipreading":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "large scale visual speech recognition")
  Member "lipreading with long short term memory":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "lipreading using temporal convolutional networks")
  Member "identity mappings in deep residual networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "overfitting in adversarially robust deep learning")
  Member "lip reading sentences in the wild":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "lipreading using temporal convolutional networks")
  Member "two stage convolutional part heatmap regression for the 1st 3d face alignment in the wild 3dfaw challenge":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "hierarchical binary cnns for landmark localization with limited resources")
  Member "neural speech recognizer acoustic to word lstm model for large vocabulary speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deep learning for audio signal processing")

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)