human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "a recurrent encoder decoder network for sequential face alignment"):
  Abstract: We propose a novel recurrent encoder-decoder network model for real-time video-based face alignment. Our proposed model predicts 2D facial point maps regularized by a regression loss, while uniquely exploiting recurrent learning at both spatial and temporal dimensions. At the spatial level, we add a feedback loop connection between the combined output response map and the input, in order to enable iterative coarse-to-fine face alignment using a single network model. At the temporal level, we first decouple the features in the bottleneck of the network into temporal-variant factors, such as pose and expression, and temporal-invariant factors, such as identity information. Temporal recurrent learning is then applied to the decoupled temporal-variant features, yielding better generalization and significantly more accurate results at test time. We perform a comprehensive experimental analysis, showing the importance of each component of our proposed model, as well as superior results over the state-of-the-art in standard datasets.

Includes members:
  Member "face alignment in full pose range a 3d total solution":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "rotate and render unsupervised photorealistic face rotation from single view images")
  Member "constrained joint cascade regression framework for simultaneous facial action unit recognition and facial landmark detection":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "jaa net joint facial action unit detection and face alignment via adaptive attention")
  Member "walk and learn facial attribute representation learning from egocentric video and contextual data":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "branched multi task networks deciding what layers to share")
  Member "weakly supervised disentangling with recurrent transformations for 3d view synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "weakly supervised disentanglement without compromises")
  Member "bayesian segnet model uncertainty in deep convolutional encoder decoder architectures for scene understanding":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "attention guided chained context aggregation for semantic segmentation")
  Member "deep cascaded regression for face alignment":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "memory efficient global refinement of decision tree ensembles and its application to face alignment")
  Member "conditional random fields as recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "real time high performance semantic image segmentation of urban street scenes")
  Member "facenet a unified embedding for face recognition and clustering":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "discrimination aware network pruning for deep model compression")

Question: Please tell me which class the source paper 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)