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: "click here human localized keypoints as guidance for viewpoint estimation"
  Abstract: We motivate and address a human-in-the-loop variant of the monocular viewpoint estimation task in which the location and class of one semantic object keypoint is available at test time. In order to leverage the keypoint information, we devise a Convolutional Neural Network called Click-Here CNN (CH-CNN) that integrates the keypoint information with activations from the layers that process the image. It transforms the keypoint information into a 2D map that can be used to weigh features from certain parts of the image more heavily. The weighted sum of these spatial features is combined with global image features to provide relevant information to the prediction layers. To train our network, we collect a novel dataset of 3D keypoint annotations on thousands of CAD models, and synthetically render millions of images with 2D keypoint information. On test instances from PASCAL 3D+, our model achieves a mean class accuracy of 90.7%, whereas the state-of-the-art baseline only obtains 85.7% mean class accuracy, justifying our argument for human-in-the-loop inference.

Connected through hyperedge HE-1 (induced by source paper "conservative wasserstein training for pose estimation"):
  with members:
    - "unsupervised domain adaptation via calibrating uncertainties"
    - "dependency aware attention control for unconstrained face recognition with image sets"
    - "deep verifier networks verification of deep discriminative models with deep generative models"
    - "confidence regularized self training"
    - "3d pose estimation and 3d model retrieval for objects in the wild"
    - "a mixed classification regression framework for 3d pose estimation from 2d images"
    - "starmap for category agnostic keypoint and viewpoint estimation"
    - "deep directional statistics pose estimation with uncertainty quantification"

Connected through hyperedge HE-2 (induced by source paper "starmap for category agnostic keypoint and viewpoint estimation"):
  with members:
    - "learning category specific mesh reconstruction from image collections"
    - "benchmarking and error diagnosis in multi instance pose estimation"
    - "unsupervised domain adaptation for 3d keypoint estimation via view consistency"
    - "towards 3d human pose estimation in the wild a weakly supervised approach"
    - "extreme clicking for efficient object annotation"
    - "learning feature pyramids for human pose estimation"
    - "convolutional networks for object category and 3d pose estimation from 2d images"
    - "multi view supervision for single view reconstruction via differentiable ray consistency"

Connected through hyperedge HE-3 (induced by source paper "semantic part detection via matching learning to generalize to novel viewpoints from limited training data"):
  with members:
    - "acquisition of localization confidence for accurate object detection"
    - "deepvoting a robust and explainable deep network for semantic part detection under partial occlusion"
    - "anchornet a weakly supervised network to learn geometry sensitive features for semantic matching"
    - "learning non maximum suppression"
    - "squeeze and excitation networks"
    - "fcss fully convolutional self similarity for dense semantic correspondence"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "scnet learning semantic correspondence"

Connected through hyperedge HE-4 (induced by source paper "adviser networks learning what question to ask for human in the loop viewpoint estimation"):
  with members:
    - "what are the visual features underlying human versus machine vision"
    - "ask the right questions active question reformulation with reinforcement learning"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "render for cnn viewpoint estimation in images using cnns trained with rendered 3d model views"
    - "adam a method for stochastic optimization"
    - "fitnets hints for thin deep nets"
    - "viewpoints and keypoints"
    - "imagenet large scale visual recognition challenge"

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)