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: "face mask extraction in video sequence"
  Abstract: Inspired by the recent development of deep network-based methods in semantic image segmentation, we introduce an end-to-end trainable model for face mask extraction in video sequence. Comparing to landmark-based sparse face shape representation, our method can produce the segmentation masks of individual facial components, which can better reflect their detailed shape variations. By integrating convolutional LSTM (ConvLSTM) algorithm with fully convolutional networks (FCN), our new ConvLSTM-FCN model works on a per-sequence basis and takes advantage of the temporal correlation in video clips. In addition, we also propose a novel loss function, called segmentation loss, to directly optimise the intersection over union (IoU) performances. In practice, to further increase segmentation accuracy, one primary model and two additional models were trained to focus on the face, eyes, and mouth regions, respectively. Our experiment shows the proposed method has achieved a 16.99% relative improvement (from 54.50 to 63.76% mean IoU) over the baseline FCN model on the 300 Videos in the Wild (300VW) dataset.

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"
    - "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"
    - "inter bmv interpolation with block motion vectors for fast semantic segmentation on video"

Connected through hyperedge HE-2 (induced by source paper "learning structure via consensus for face segmentation and parsing"):
  with members:
    - "fsgan subject agnostic face swapping and reenactment"
    - "does generative face completion help face recognition"
    - "learning discriminators as energy networks in adversarial learning"
    - "adversarial structure matching loss for image segmentation"
    - "adaptive affinity fields for semantic segmentation"
    - "face completion with semantic knowledge and collaborative adversarial learning"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "adversarial learning for semi supervised semantic 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)