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: "an efficient deep learning hashing neural network for mobile visual search"
  Abstract: Mobile visual search applications are emerging that enable users to sense their surroundings with smart phones. However, because of the particular challenges of mobile visual search, achieving a high recognition bitrate has becomes a consistent target of previous related works. In this paper, we propose a few-parameter, low-latency, and high-accuracy deep hashing approach for constructing binary hash codes for mobile visual search. First, we exploit the architecture of the MobileNet model, which significantly decreases the latency of deep feature extraction by reducing the number of model parameters while maintaining accuracy. Second, we add a hash-like layer into MobileNet to train the model on labeled mobile visual data. Evaluations show that the proposed system can exceed state-of-the-art accuracy performance in terms of the MAP. More importantly, the memory consumption is much less than that of other deep learning models. The proposed method requires only $13$ MB of memory for the neural network and achieves a MAP of $97.80\%$ on the mobile location recognition dataset used for testing.

Connected through hyperedge HE-1 (induced by source paper "anchor transform learning sparse representations of discrete objects"):
  with members:
    - "neural input search for large scale recommendation models"
    - "how large a vocabulary does text classification need a variational approach to vocabulary selection"
    - "word embedding based on low rank doubly stochastic matrix decomposition"
    - "bayesian compression for natural language processing"
    - "probabilistic fasttext for multi sense word embeddings"
    - "adaptive input representations for neural language modeling"
    - "towards learning sparsely used dictionaries with arbitrary supports"
    - "attention is all you need"

Connected through hyperedge HE-2 (induced by source paper "deep learning acceleration techniques for real time mobile vision applications"):
  with members:
    - "convolutional neural network for intrusion detection system in cyber physical systems"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "towards high performance video object detection for mobiles"
    - "ai benchmark running deep neural networks on android smartphones"
    - "towards a theoretical understanding of hashing based neural nets"
    - "tiny ssd a tiny single shot detection deep convolutional neural network for real time embedded object detection"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "deep reinforcement learning for visual object tracking in videos"

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)