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: "resource constrained neural network architecture search"
  Abstract: The design of neural network architectures is frequently either based on human expertise using trial/error and empirical feedback or tackled via large scale reinforcement learning strategies run over distinct discrete architecture choices. In the latter case, the optimization task is non-differentiable and also not very amenable to derivative-free optimization methods. Most methods in use today require exorbitant computational resources. And if we want networks that additionally satisfy resource constraints, the above challenges are exacerbated because the search procedure must now balance accuracy with certain budget constraints on resources. We formulate this problem as the optimization of a set function - we find that the empirical behavior of this set function often (but not always) satisfies marginal gain and monotonicity principles - properties central to the idea of submodularity. Based on this observation, we adapt algorithms that are well-known within discrete optimization to obtain heuristic schemes for neural network architecture search, with resource constraints on the architecture. This simple scheme when applied on CIFAR-100 and ImageNet, identifies resource-constrained architectures with quantifiably better performance than current state-of-the-art models designed for mobile devices. Specifically, we find high-performing architectures with fewer parameters and computations by a search method that is much faster.

Connected through hyperedge HE-1 (induced by source paper "skynet a hardware efficient method for object detection and tracking on embedded systems"):
  with members:
    - "accuracy vs efficiency achieving both through fpga implementation aware neural architecture search"
    - "got 10k a large high diversity benchmark for generic object tracking in the wild"
    - "skynet a champion model for dac sdc on low power object detection"
    - "single path nas device aware efficient convnet design"
    - "ternary hybrid neural tree networks for highly constrained iot applications"
    - "searching for mobilenetv3"
    - "fcos fully convolutional one stage object detection"
    - "scale aware trident networks for object detection"

Connected through hyperedge HE-2 (induced by source paper "cars continuous evolution for efficient neural architecture search"):
  with members:
    - "towards optimal structured cnn pruning via generative adversarial learning"
    - "progressive darts bridging the optimization gap for nas in the wild"
    - "autogan neural architecture search for generative adversarial networks"
    - "auto reid searching for a part aware convnet for person re identification"
    - "single path one shot neural architecture search with uniform sampling"
    - "universally slimmable networks and improved training techniques"
    - "autoslim towards one shot architecture search for channel numbers"
    - "learning sparse networks using targeted dropout"

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)