human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "robust end to end deep audiovisual speech recognition"
  Abstract: Speech is one of the most effective ways of communication among humans. Even though audio is the most common way of transmitting speech, very important information can be found in other modalities, such as vision. Vision is particularly useful when the acoustic signal is corrupted. Multi-modal speech recognition however has not yet found wide-spread use, mostly because the temporal alignment and fusion of the different information sources is challenging. #R##N#This paper presents an end-to-end audiovisual speech recognizer (AVSR), based on recurrent neural networks (RNN) with a connectionist temporal classification (CTC) loss function. CTC creates sparse "peaky" output activations, and we analyze the differences in the alignments of output targets (phonemes or visemes) between audio-only, video-only, and audio-visual feature representations. We present the first such experiments on the large vocabulary IBM ViaVoice database, which outperform previously published approaches on phone accuracy in clean and noisy conditions.

Connected through hyperedge HE-1 (induced by source paper "lcanet end to end lipreading with cascaded attention ctc"):
  with members:
    - "progressive joint modeling in unsupervised single channel overlapped speech recognition"
    - "draps dynamic and resource aware placement scheme for docker containers in a heterogeneous cluster"
    - "deeprebirth accelerating deep neural network execution on mobile devices"
    - "attention is all you need"
    - "lifting from the deep convolutional 3d pose estimation from a single image"
    - "end to end visual speech recognition with lstms"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "reconstruction for feature disentanglement in pose invariant face recognition"

Connected through hyperedge HE-2 (induced by source paper "noise tolerant audio visual online person verification using an attention based neural network fusion"):
  with members:
    - "learning to localize sound source in visual scenes"
    - "seeing voices and hearing faces cross modal biometric matching"
    - "end to end audiovisual speech recognition"
    - "voxceleb2 deep speaker recognition"
    - "joint 3d face reconstruction and dense alignment with position map regression network"
    - "on learning associations of faces and voices"
    - "voxceleb a large scale speaker identification dataset"
    - "moon a mixed objective optimization network for the recognition of facial attributes"

Question: Please tell me which class the center node 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.CL(Computation and Language)