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 alignment robust to pose expressions and occlusions"
  Abstract: We propose an Ensemble of Robust Constrained Local Models for alignment of faces in the presence of significant occlusions and of any unknown pose and expression. To account for partial occlusions we introduce, Robust Constrained Local Models, that comprises of a deformable shape and local landmark appearance model and reasons over binary occlusion labels. Our occlusion reasoning proceeds by a hypothesize-and-test search over occlusion labels. Hypotheses are generated by Constrained Local Model based shape fitting over randomly sampled subsets of landmark detector responses and are evaluated by the quality of face alignment. To span the entire range of facial pose and expression variations we adopt an ensemble of independent Robust Constrained Local Models to search over a discretized representation of pose and expression. We perform extensive evaluation on a large number of face images, both occluded and unoccluded. We find that our face alignment system trained entirely on facial images captured "in-the-lab" exhibits a high degree of generalization to facial images captured "in-the-wild". Our results are accurate and stable over a wide spectrum of occlusions, pose and expression variations resulting in excellent performance on many real-world face datasets.

Connected through hyperedge HE-1 (induced by source paper "tree gated deep regressor ensemble for face alignment in the wild"):
  with members:
    - "adaptive neural trees"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "pose invariant face alignment with a single cnn"
    - "improving landmark localization with semi supervised learning"
    - "decision forests convolutional networks and the models in between"
    - "learning deep representation for face alignment with auxiliary attributes"
    - "adam a method for stochastic optimization"
    - "learning factored representations in a deep mixture of experts"

Connected through hyperedge HE-2 (induced by source paper "disentangling 3d pose in a dendritic cnn for unconstrained 2d face alignment"):
  with members:
    - "faster than real time facial alignment a 3d spatial transformer network approach in unconstrained poses"
    - "large pose 3d face reconstruction from a single image via direct volumetric cnn regression"
    - "pose invariant face alignment with a single cnn"
    - "deep heterogeneous feature fusion for template based face recognition"
    - "the do s and don ts for cnn based face verification"
    - "dense face alignment"
    - "simultaneous facial landmark detection pose and deformation estimation under facial occlusion"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"

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)