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: "unifying and merging well trained deep neural networks for inference stage"
  Abstract: We propose a novel method to merge convolutional neural-nets for the inference stage. Given two well-trained networks that may have different architectures that handle different tasks, our method aligns the layers of the original networks and merges them into a unified model by sharing the representative codes of weights. The shared weights are further re-trained to fine-tune the performance of the merged model. The proposed method effectively produces a compact model that may run original tasks simultaneously on resource-limited devices. As it preserves the general architectures and leverages the co-used weights of well-trained networks, a substantial training overhead can be reduced to shorten the system development time. Experimental results demonstrate a satisfactory performance and validate the effectiveness of the method.

Connected through hyperedge HE-1 (induced by source paper "feature partitioning for efficient multi task architectures"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "multi task zipping via layer wise neuron sharing"
    - "simple random search provides a competitive approach to reinforcement learning"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "combating catastrophic forgetting with developmental compression"
    - "towards automated deep learning efficient joint neural architecture and hyperparameter search"
    - "evolutionary architecture search for deep multitask networks"
    - "regularized evolution for image classifier architecture search"

Connected through hyperedge HE-2 (induced by source paper "disentangling redundancy for multi task pruning"):
  with members:
    - "branched multi task networks deciding what layers to share"
    - "multi task zipping via layer wise neuron sharing"
    - "nestdnn resource aware multi tenant on device deep learning for continuous mobile vision"
    - "compressing neural networks using the variational information bottleneck"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "estimating mixture entropy with pairwise distances"
    - "learning structured sparsity in deep neural networks"
    - "eie efficient inference engine on compressed deep neural network"

Connected through hyperedge HE-3 (induced by source paper "network transplanting"):
  with members:
    - "meta continual learning"
    - "progressive neural architecture search"
    - "towards interpretable r cnn by unfolding latent structures"
    - "learning transferable architectures for scalable image recognition"
    - "progressive neural networks"
    - "opening the black box of deep neural networks via information"
    - "pathnet evolution channels gradient descent in super neural networks"
    - "learning to reinforcement learn"

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)