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: "reformulating level sets as deep recurrent neural network approach to semantic segmentation"
  Abstract: Variational Level Set (LS) has been a widely used method in medical segmentation. However, it is limited when dealing with multi-instance objects in the real world. In addition, its segmentation results are quite sensitive to initial settings and highly depend on the number of iterations. To address these issues and boost the classic variational LS methods to a new level of the learnable deep learning approaches, we propose a novel definition of contour evolution named Recurrent Level Set (RLS)} to employ Gated Recurrent Unit under the energy minimization of a variational LS functional. The curve deformation process in RLS is formed as a hidden state evolution procedure and updated by minimizing an energy functional composed of fitting forces and contour length. By sharing the convolutional features in a fully end-to-end trainable framework, we extend RLS to Contextual RLS (CRLS) to address semantic segmentation in the wild. The experimental results have shown that our proposed RLS improves both computational time and segmentation accuracy against the classic variations LS-based method, whereas the fully end-to-end system CRLS achieves competitive performance compared to the state-of-the-art semantic segmentation approaches.

Connected through hyperedge HE-1 (induced by source paper "seesawfacenets sparse and robust face verification model for mobile platform"):
  with members:
    - "seesaw net convolution neural network with uneven group convolution"
    - "shrinkteanet million scale lightweight face recognition via shrinking teacher student networks"
    - "searching for mobilenetv3"
    - "airface lightweight and efficient model for face recognition"
    - "igcv3 interleaved low rank group convolutions for efficient deep neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "deep recurrent level set for segmenting brain tumors"
    - "cosface large margin cosine loss for deep face recognition"

Connected through hyperedge HE-2 (induced by source paper "mobinet a mobile binary network for image classification"):
  with members:
    - "feature selective anchor free module for single shot object detection"
    - "espnet efficient spatial pyramid of dilated convolutions for semantic segmentation"
    - "rmdl random multimodel deep learning for classification"
    - "amc automl for model compression and acceleration on mobile devices"
    - "bounding box regression with uncertainty for accurate object detection"
    - "bi real net enhancing the performance of 1 bit cnns with improved representational capability and advanced training algorithm"
    - "seeing small faces from robust anchor s perspective"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"

Connected through hyperedge HE-3 (induced by source paper "mobiface a lightweight deep learning face recognition on mobile devices"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "deep recurrent level set for segmenting brain tumors"
    - "cosface large margin cosine loss for deep face recognition"
    - "seeing small faces from robust anchor s perspective"
    - "quantization mimic towards very tiny cnn for object detection"
    - "mobilefacenets efficient cnns for accurate real time face verification on mobile devices"
    - "fd mobilenet improved mobilenet with a fast downsampling strategy"
    - "learning efficient convolutional networks through network slimming"

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)