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: "speeding up neural networks for large scale classification using wta hashing"
  Abstract: In this paper we propose to use the Winner Takes All hashing technique to speed up forward propagation and backward propagation in fully connected layers in convolutional neural networks. The proposed technique reduces significantly the computational complexity, which in turn, allows us to train layers with a large number of kernels with out the associated time penalty. #R##N#As a consequence we are able to train convolutional neural network on a very large number of output classes with only a small increase in the computational cost. To show the effectiveness of the technique we train a new output layer on a pretrained network using both the regular multiplicative approach and our proposed hashing methodology. Our results showed no drop in performance and demonstrate, with our implementation, a 7 fold speed up during the training.

Connected through hyperedge HE-1 (induced by source paper "adaptive sampled softmax with kernel based sampling"):
  with members:
    - "clustering is efficient for approximate maximum inner product search"
    - "strategies for training large vocabulary neural language models"
    - "an exploration of softmax alternatives belonging to the spherical loss family"
    - "efficient exact gradient update for training deep networks with very large sparse targets"
    - "recurrent neural network regularization"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-2 (induced by source paper "acdc a structured efficient linear layer"):
  with members:
    - "random projections through multiple optical scattering approximating kernels at the speed of light"
    - "end to end memory networks"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "tensorizing neural networks"
    - "learning both weights and connections for efficient neural networks"
    - "compressing neural networks with the hashing trick"
    - "towards trainable media using waves for neural network style training"
    - "an exploration of parameter redundancy in deep networks with circulant projections"

Connected through hyperedge HE-3 (induced by source paper "exploiting local structures with the kronecker layer in convolutional networks"):
  with members:
    - "learning both weights and connections for efficient neural networks"
    - "spatial transformer networks"
    - "high performance offline handwritten chinese character recognition using googlenet and directional feature maps"
    - "compressing neural networks with the hashing trick"
    - "accelerating very deep convolutional networks for classification and detection"
    - "deep learning with limited numerical precision"
    - "exploiting linear structure within convolutional networks for efficient evaluation"
    - "theano based large scale visual recognition with multiple gpus"

Connected through hyperedge HE-4 (induced by source paper "deep fried convnets"):
  with members:
    - "learning both weights and connections for efficient neural networks"
    - "compressing neural networks with the hashing trick"
    - "exploiting linear structure within convolutional networks for efficient evaluation"
    - "how transferable are features in deep neural networks"
    - "fitnets hints for thin deep nets"
    - "going deeper with convolutions"
    - "a la carte learning fast kernels"
    - "one weird trick for parallelizing convolutional neural networks"

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)