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: "human and sheep facial landmarks localisation by triplet interpolated features"
  Abstract: In this paper we present a method for localisation of facial landmarks on human and sheep. We introduce a new feature extraction scheme called triplet-interpolated feature used at each iteration of the cascaded shape regression framework. It is able to extract features from similar semantic location given an estimated shape, even when head pose variations are large and the facial landmarks are very sparsely distributed. Furthermore, we study the impact of training data imbalance on model performance and propose a training sample augmentation scheme that produces more initialisations for training samples from the minority. More specifically, the augmentation number for a training sample is made to be negatively correlated to the value of the fitted probability density function at the sample's position. We evaluate the proposed scheme on both human and sheep facial landmarks localisation. On the benchmark 300w human face dataset, we demonstrate the benefits of our proposed methods and show very competitive performance when comparing to other methods. On a newly created sheep face dataset, we get very good performance despite the fact that we only have a limited number of training samples and a set of sparse landmarks are annotated.

Connected through hyperedge HE-1 (induced by source paper "transferring dense pose to proximal animal classes"):
  with members:
    - "semi supervised segmentation of salt bodies in seismic images using an ensemble of convolutional neural networks"
    - "deep learning tools for the measurement of animal behavior in neuroscience"
    - "billion scale semi supervised learning for image classification"
    - "unsupervised part based disentangling of object shape and appearance"
    - "three d safari learning to estimate zebra pose shape and texture from images in the wild"
    - "slim densepose thrifty learning from sparse annotations and motion cues"
    - "c3dpo canonical 3d pose networks for non rigid structure from motion"
    - "unsupervised learning of landmarks by descriptor vector exchange"

Connected through hyperedge HE-2 (induced by source paper "cross domain adaptation for animal pose estimation"):
  with members:
    - "weakly and semi supervised human body part parsing via pose guided knowledge transfer"
    - "cross domain weakly supervised object detection through progressive domain adaptation"
    - "multi evidence filtering and fusion for multi label classification object detection and semantic segmentation based on weakly supervised learning"
    - "3d human pose estimation in the wild by adversarial learning"
    - "partial adversarial domain adaptation"
    - "instagan instance aware image to image translation"
    - "deep adversarial attention alignment for unsupervised domain adaptation the benefit of target expectation maximization"
    - "cross modal deep variational hand pose estimation"

Connected through hyperedge HE-3 (induced by source paper "animalweb a large scale hierarchical dataset of annotated animal faces"):
  with members:
    - "quantized densely connected u nets for efficient landmark localization"
    - "multi attention multi class constraint for fine grained image recognition"
    - "how far are we from solving the 2d 3d face alignment problem and a dataset of 230 000 3d facial landmarks"
    - "interspecies knowledge transfer for facial keypoint detection"
    - "the inaturalist species classification and detection dataset"
    - "occlusion coherence detecting and localizing occluded faces"
    - "deep residual learning for image recognition"
    - "wider face a face detection benchmark"

Connected through hyperedge HE-4 (induced by source paper "a two step learning method for detecting landmarks on faces from different domains"):
  with members:
    - "interspecies knowledge transfer for facial keypoint detection"
    - "deep reconstruction classification networks for unsupervised domain adaptation"

Connected through hyperedge HE-5 (induced by source paper "interspecies knowledge transfer for facial keypoint detection"):
  with members:
    - "constrained joint cascade regression framework for simultaneous facial action unit recognition and facial landmark detection"
    - "a recurrent encoder decoder network for sequential face alignment"
    - "facenet2expnet regularizing a deep face recognition net for expression recognition"
    - "do we really need to collect millions of faces for effective face recognition"
    - "what makes imagenet good for transfer learning"
    - "warpnet weakly supervised matching for single view reconstruction"
    - "robust face alignment using a mixture of invariant experts"
    - "joint face detection and alignment using multitask cascaded convolutional networks"

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)