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: "convolutional simplex projection network cspn for weakly supervised semantic segmentation"
  Abstract: Weakly supervised semantic segmentation has been a subject of increased interest due to the scarcity of fully annotated images. We introduce a new approach for solving weakly supervised semantic segmentation with deep Convolutional Neural Networks (CNNs). The method introduces a novel layer which applies simplex projection on the output of a neural network using area constraints of class objects. The proposed method is general and can be seamlessly integrated into any CNN architecture. Moreover, the projection layer allows strongly supervised models to be adapted to weakly supervised models effortlessly by substituting ground truth labels. Our experiments have shown that applying such an operation on the output of a CNN improves the accuracy of semantic segmentation in a weakly supervised setting with image-level labels.

Connected through hyperedge HE-1 (induced by source paper "homogeneous linear inequality constraints for neural network activations"):
  with members:
    - "imposing hard constraints on deep networks promises and limitations"
    - "optnet differentiable optimization as a layer in neural networks"
    - "se3 nets learning rigid body motion using deep neural networks"
    - "deep kinematic pose regression"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "constrained convolutional neural networks for weakly supervised segmentation"
    - "adam a method for stochastic optimization"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-2 (induced by source paper "cian cross image affinity net for weakly supervised semantic segmentation"):
  with members:
    - "ficklenet weakly and semi supervised semantic image segmentation using stochastic inference"
    - "semantic aware attention based deep object co segmentation"
    - "ocnet object context network for scene parsing"
    - "learning pixel level semantic affinity with image level supervision for weakly supervised semantic segmentation"
    - "weakly supervised semantic segmentation by iteratively mining common object features"
    - "adversarial complementary learning for weakly supervised object localization"
    - "revisiting dilated convolution a simple approach for weakly and semi supervised semantic segmentation"
    - "dual attention network for scene segmentation"

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)