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: "object recognition from short videos for robotic perception"
  Abstract: Deep neural networks have become the primary learning technique for object recognition. Videos, unlike still images, are temporally coherent which makes the application of deep networks non-trivial. Here, we investigate how motion can aid object recognition in short videos. Our approach is based on Long Short-Term Memory (LSTM) deep networks. Unlike previous applications of LSTMs, we implement each gate as a convolution. We show that convolutional-based LSTM models are capable of learning motion dependencies and are able to improve the recognition accuracy when more frames in a sequence are available. We evaluate our approach on the Washington RGBD Object dataset and on the Washington RGBD Scenes dataset. Our approach outperforms deep nets applied to still images and sets a new state-of-the-art in this domain.

Connected through hyperedge HE-1 (induced by source paper "sequence to sequence models can directly translate foreign speech"):
  with members:
    - "towards speech to text translation without speech recognition"
    - "towards better decoding and language model integration in sequence to sequence models"
    - "very deep convolutional networks for end to end speech recognition"
    - "an unsupervised probability model for speech to translation alignment of low resource languages"
    - "chained predictions using convolutional neural networks"
    - "listen and translate a proof of concept for end to end speech to text translation"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "google s multilingual neural machine translation system enabling zero shot translation"

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.CV(Computer Vision and Pattern Recognition)