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: "end to end semantic face segmentation with conditional random fields as convolutional recurrent and adversarial networks"
  Abstract: Recent years have seen a sharp increase in the number of related yet distinct advances in semantic segmentation. Here, we tackle this problem by leveraging the respective strengths of these advances. That is, we formulate a conditional random field over a four-connected graph as end-to-end trainable convolutional and recurrent networks, and estimate them via an adversarial process. Importantly, our model learns not only unary potentials but also pairwise potentials, while aggregating multi-scale contexts and controlling higher-order inconsistencies. We evaluate our model on two standard benchmark datasets for semantic face segmentation, achieving state-of-the-art results on both of them.

Connected through hyperedge HE-1 (induced by source paper "dynamic face video segmentation via reinforcement learning"):
  with members:
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "face mask extraction in video sequence"
    - "architecture search of dynamic cells for semantic video segmentation"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "meta learning transferable active learning policies by deep reinforcement learning"
    - "dynamic video segmentation network"
    - "low latency video semantic segmentation"
    - "light weight refinenet for real time semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "face mask extraction in video sequence"):
  with members:
    - "bringing background into the foreground making all classes equal in weakly supervised video semantic segmentation"
    - "a review on deep learning techniques applied to semantic segmentation"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "clockwork convnets for video semantic segmentation"
    - "one shot video object segmentation"
    - "semi supervised domain adaptation for weakly labeled semantic video object segmentation"
    - "visual speech recognition using pca networks and lstms in a tandem gmm hmm system"
    - "joint face detection and alignment using multitask cascaded convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "face parsing with roi tanh warping"):
  with members:
    - "integrated face analytics networks through cross dataset hybrid training"
    - "face parsing via a fully convolutional continuous crf neural network"
    - "generative face completion"
    - "attention is all you need"
    - "face parsing via recurrent propagation"
    - "mask r cnn"
    - "boundary aware instance segmentation"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"

Connected through hyperedge HE-4 (induced by source paper "learning segmentation masks with the independence prior"):
  with members:
    - "weakly supervised instance segmentation using class peak response"
    - "st gan spatial transformer generative adversarial networks for image compositing"
    - "learning pixel level semantic affinity with image level supervision for weakly supervised semantic segmentation"
    - "spectral normalization for generative adversarial networks"
    - "annotation free and one shot learning for instance segmentation of homogeneous object clusters"
    - "mine mutual information neural estimation"
    - "image generation from scene graphs"
    - "relation networks for object detection"

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)