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: "growing regression forests by classification applications to object pose estimation"
  Abstract: In this work, we propose a novel node splitting method for regression trees and incorporate it into the regression forest framework. Unlike traditional binary splitting, where the splitting rule is selected from a predefined set of binary splitting rules via trial-and-error, the proposed node splitting method first finds clusters of the training data which at least locally minimize the empirical loss without considering the input space. Then splitting rules which preserve the found clusters as much as possible are determined by casting the problem into a classification problem. Consequently, our new node splitting method enjoys more freedom in choosing the splitting rules, resulting in more efficient tree structures. In addition to the Euclidean target space, we present a variant which can naturally deal with a circular target space by the proper use of circular statistics. We apply the regression forest employing our node splitting to head pose estimation (Euclidean target space) and car direction estimation (circular target space) and demonstrate that the proposed method significantly outperforms state-of-the-art methods (38.5% and 22.5% error reduction respectively).

Connected through hyperedge HE-1 (induced by source paper "wing loss for robust facial landmark localisation with convolutional neural networks"):
  with members:
    - "evaluation of dense 3d reconstruction from 2d face images in the wild"
    - "faster than real time facial alignment a 3d spatial transformer network approach in unconstrained poses"
    - "sphereface deep hypersphere embedding for face recognition"
    - "end to end 3d face reconstruction with deep neural networks"
    - "pose invariant face alignment with a single cnn"
    - "how far are we from solving the 2d 3d face alignment problem and a dataset of 230 000 3d facial landmarks"
    - "constrained joint cascade regression framework for simultaneous facial action unit recognition and facial landmark detection"
    - "dense face alignment"

Connected through hyperedge HE-2 (induced by source paper "holoface augmenting human to human interactions on hololens"):
  with members:
    - "deep alignment network a convolutional neural network for robust face alignment"
    - "pose invariant face alignment with a single cnn"
    - "binarized convolutional landmark localizers for human pose estimation and face alignment with limited resources"
    - "joint face detection and alignment using multitask cascaded convolutional networks"
    - "face alignment using k cluster regression forests with weighted splitting"
    - "wider face a face detection benchmark"

Connected through hyperedge HE-3 (induced by source paper "deep regression forests for age estimation"):
  with members:
    - "deep label distribution learning with label ambiguity"
    - "label distribution learning forests"
    - "r fcn object detection via region based fully convolutional networks"
    - "some like it hot visual guidance for preference prediction"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "label distribution learning"
    - "very deep convolutional networks for large scale image recognition"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-4 (induced by source paper "direct multitype cardiac indices estimation via joint representation and regression learning"):
  with members:
    - "a fully convolutional neural network for cardiac segmentation in short axis mri"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "a combined deep learning and deformable model approach to fully automatic segmentation of the left ventricle in cardiac mri"
    - "learning deconvolution network for semantic segmentation"
    - "caffe convolutional architecture for fast feature embedding"
    - "visualizing and understanding convolutional networks"

Connected through hyperedge HE-5 (induced by source paper "discriminative sparse neighbor approximation for imbalanced learning"):
  with members:
    - "holistically nested edge detection"
    - "high for low and low for high efficient boundary detection from deep object features and its applications to high level vision"
    - "deepedge a multi scale bifurcated deep network for top down contour detection"
    - "oriented edge forests for boundary detection"
    - "learning mid level features and modeling neuron selectivity for image classification"
    - "fast edge detection using structured forests"
    - "smote synthetic minority over sampling technique"

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)