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: "recurrent segmentation for variable computational budgets"
  Abstract: State-of-the-art systems for semantic image segmentation utilize feed-forward pipelines with fixed computational costs. Building an image segmentation system that works across a range of computational budgets is challenging and time-intensive as new architectures must be designed and trained for every computational setting. To address this problem we develop a recurrent neural network that successively improves prediction quality with each iteration. Importantly, the RNN may be employed across a range of computational budgets by merely running the model for varying numbers of iterations. The RNN achieves results comparable to state-of-the-art systems in image segmentation on PASCAL VOC 2012 and Cityscapes segmentation datasets, however the RNN performs this task with a fraction of the computational budget. Finally, we demonstrate how one may exploit the properties of the RNN to efficiently perform video segmentation with half the computational cost of a comparable, state-of-the-art image segmentation method.

Connected through hyperedge HE-1 (induced by source paper "distributed iterative gating networks for semantic segmentation"):
  with members:
    - "gated multiple feedback network for image super resolution"
    - "recurrent iterative gating networks for semantic segmentation"
    - "large kernel matters improve semantic segmentation by global convolutional network"
    - "object region mining with adversarial erasing a simple classification to semantic segmentation approach"
    - "recurrent scene parsing with perspective understanding in the loop"
    - "convolutional networks with adaptive inference graphs"
    - "iterative deep convolutional encoder decoder network for medical image segmentation"
    - "not all pixels are equal difficulty aware semantic segmentation via deep layer cascade"

Connected through hyperedge HE-2 (induced by source paper "model slicing for supporting complex analytics with elastic inference cost and resource constraints"):
  with members:
    - "isbnet instance aware selective branching networks"
    - "group normalization"
    - "slimmable neural networks"
    - "skipnet learning dynamic routing in convolutional networks"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "learning efficient convolutional networks through network slimming"
    - "learning anytime predictions in neural networks via adaptive loss balancing"

Connected through hyperedge HE-3 (induced by source paper "canet class agnostic segmentation networks with iterative refinement and attentive few shot learning"):
  with members:
    - "weakly supervised semantic segmentation by iteratively mining common object features"
    - "prototypical networks for few shot learning"
    - "learning to segment every thing"
    - "one shot learning for semantic segmentation"
    - "rethinking atrous convolution for semantic image segmentation"
    - "mask r cnn"
    - "learning feed forward one shot learners"
    - "matching networks for one shot learning"

Connected through hyperedge HE-4 (induced by source paper "improved techniques for training adaptive deep networks"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "knowledge distillation by on the fly native ensemble"
    - "amc automl for model compression and acceleration on mobile devices"
    - "pixel wise attentional gating for parsimonious pixel labeling"
    - "skipnet learning dynamic routing in convolutional networks"
    - "learning transferable architectures for scalable image recognition"
    - "blockdrop dynamic inference paths in residual networks"
    - "branchynet fast inference via early exiting from deep 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)