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 "mutual information maximization for effective lip reading"):
  Abstract: Lip reading has received an increasing research interest in recent years due to the rapid development of deep learning and its widespread potential applications. One key point to obtain good performance for the lip reading task depends heavily on how effective the representation can be to capture the lip movement information and meanwhile to resist the noises resulted from the change of pose, lighting conditions, speaker's appearance and so on. Towards this target, we propose to introduce the mutual information constraints on both the local feature's level and the global sequence's level to enhance the relations of the features with the speech content. On the one hand, we constraint the features generated at each time step to enable them carry a strong relation with the speech content by imposing the local mutual information maximization constraint (LMIM), leading to improvements over the model's ability to discover fine-grained lip movements and the fine-grained differences among words with similar pronunciation, such as ``spend'' and ``spending''. On the other hand, we introduce the mutual information maximization constraint on the global sequence's level (GMIM), to make the model be able to pay more attention to discriminate key frames related with the speech content, and less to various noises appeared in the speaking process. By combining these two advantages together, the proposed method is expected to be both discriminative and robust for effective lip reading. To verify this method, we evaluate on two large-scale benchmark. We perform a detailed analysis and comparison on several aspects, including the comparison of the LMIM and GMIM with the baseline, the visualization of the learned representation and so on. The results not only prove the effectiveness of the proposed method but also report new state-of-the-art performance on both the two benchmarks.

Includes members:
  Member "multi grained spatio temporal modeling for lip reading":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "lipreading using temporal convolutional networks")
  Member "information maximizing visual question generation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "heterogeneous graph learning for visual commonsense reasoning")
  Member "high resolution talking face generation via mutual information approximation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "everybody s talkin let me talk as you want")
  Member "lrs3 ted a large scale dataset for visual speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep audio visual speech recognition")
  Member "end to end audiovisual speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "lipreading using temporal convolutional networks")
  Member "lcanet end to end lipreading with cascaded attention ctc":
    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 "lrw 1000 a naturally distributed large scale benchmark for lip reading in the wild":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "lipreading using temporal convolutional networks")
  Member "pushing the boundaries of audiovisual word recognition using residual networks and lstms":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "can we read speech beyond the lips rethinking roi selection for deep visual speech recognition")

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)