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: "binarized convolutional neural networks with separable filters for efficient hardware acceleration"
  Abstract: State-of-the-art convolutional neural networks are enormously costly in both compute and memory, demanding massively parallel GPUs for execution. Such networks strain the computational capabilities and energy available to embedded and mobile processing platforms, restricting their use in many important applications. In this paper, we push the boundaries of hardware-effective CNN design by proposing BCNN with Separable Filters (BCNNw/SF), which applies Singular Value Decomposition (SVD) on BCNN kernels to further reduce computational and storage complexity. To enable its implementation, we provide a closed form of the gradient over SVD to calculate the exact gradient with respect to every binarized weight in backward propagation. We verify BCNNw/SF on the MNIST, CIFAR-10, and SVHN datasets, and implement an accelerator for CIFAR-10 on FPGA hardware. Our BCNNw/SF accelerator realizes memory savings of 17% and execution time reduction of 31.3% compared to BCNN with only minor accuracy sacrifices.

Connected through hyperedge HE-1 (induced by source paper "accelerating deterministic and stochastic binarized neural networks on fpgas using opencl"):
  with members:
    - "on the origin of deep learning"
    - "a gpu outperforming fpga accelerator architecture for binary convolutional neural networks"
    - "binaryconnect training deep neural networks with binary weights during propagations"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-2 (induced by source paper "packing sparse convolutional neural networks for efficient systolic array implementations column combining under joint optimization"):
  with members:
    - "design flow of accelerating hybrid extremely low bit width neural network in embedded fpga"
    - "block sparse recurrent neural networks"
    - "in datacenter performance analysis of a tensor processing unit"
    - "learning efficient convolutional networks through network slimming"
    - "shift a zero flop zero parameter alternative to spatial convolutions"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "chain nn an energy efficient 1d chain architecture for accelerating deep convolutional neural networks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-3 (induced by source paper "intelligence beyond the edge inference on intermittent embedded systems"):
  with members:
    - "alpaca intermittent execution without checkpoints"
    - "in datacenter performance analysis of a tensor processing unit"
    - "ray a distributed framework for emerging ai applications"
    - "scnn an accelerator for compressed sparse convolutional neural networks"
    - "c ir cnn accelerating and compressing deep neural networks using block circulant weight matrices"
    - "lightnn filling the gap between conventional deep neural networks and binarized networks"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "inception v4 inception resnet and the impact of residual connections on learning"

Connected through hyperedge HE-4 (induced by source paper "reservoir computing hardware with cellular automata"):
  with members:
    - "deep reservoir computing using cellular automata"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "connectionist symbolic machine intelligence using cellular automata based reservoir hyperdimensional computing"
    - "adam a method for stochastic optimization"

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)