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: "starmap for category agnostic keypoint and viewpoint estimation"
  Abstract: Semantic keypoints provide concise abstractions for a variety of visual understanding tasks. Existing methods define semantic keypoints separately for each category with a fixed number of semantic labels in fixed indices. As a result, this keypoint representation is in-feasible when objects have a varying number of parts, e.g. chairs with varying number of legs. We propose a category-agnostic keypoint representation, which combines a multi-peak heatmap (StarMap) for all the keypoints and their corresponding features as 3D locations in the canonical viewpoint (CanViewFeature) defined for each instance. Our intuition is that the 3D locations of the keypoints in canonical object views contain rich semantic and compositional information. Using our flexible representation, we demonstrate competitive performance in keypoint detection and localization compared to category-specific state-of-the-art methods. Moreover, we show that when augmented with an additional depth channel (DepthMap) to lift the 2D keypoints to 3D, our representation can achieve state-of-the-art results in viewpoint estimation. Finally, we show that our category-agnostic keypoint representation can be generalized to novel categories.

Connected through hyperedge HE-1 (induced by source paper "objects as points"):
  with members:
    - "feature selective anchor free module for single shot object detection"
    - "scale aware trident networks for object detection"
    - "bottom up object detection by grouping extreme and center points"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "cornernet detecting objects as paired keypoints"
    - "yolov3 an incremental improvement"
    - "simple baselines for human pose estimation and tracking"
    - "acquisition of localization confidence for accurate object detection"

Connected through hyperedge HE-2 (induced by source paper "patch based 3d human pose refinement"):
  with members:
    - "pose guided structured region ensemble network for cascaded hand pose estimation"
    - "monocular 3d human pose estimation by generation and ordinal ranking"
    - "self supervised learning of 3d human pose using multi view geometry"
    - "ordinal depth supervision for 3d human pose estimation"
    - "rethinking pose in 3d multi stage refinement and recovery for markerless motion capture"
    - "task oriented hand motion retargeting for dexterous manipulation imitation"
    - "posefix model agnostic general human pose refinement network"
    - "bodynet volumetric inference of 3d human body shapes"

Connected through hyperedge HE-3 (induced by source paper "bottom up object detection by grouping extreme and center points"):
  with members:
    - "cornernet detecting objects as paired keypoints"
    - "personlab person pose estimation and instance segmentation with a bottom up part based geometric embedding model"
    - "yolov3 an incremental improvement"
    - "simple baselines for human pose estimation and tracking"
    - "acquisition of localization confidence for accurate object detection"
    - "path aggregation network for instance segmentation"
    - "couplenet coupling global structure with local parts for object detection"
    - "improving object localization with fitness nms and bounded iou loss"

Connected through hyperedge HE-4 (induced by source paper "centerface joint face detection and alignment using face as point"):
  with members:
    - "feature selective anchor free module for single shot object detection"
    - "consistent optimization for single shot object detection"
    - "retinaface single stage dense face localisation in the wild"
    - "lffd a light and fast face detector for edge devices"
    - "objects as points"
    - "bottom up object detection by grouping extreme and center points"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "cornernet detecting objects as paired keypoints"

Connected through hyperedge HE-5 (induced by source paper "pose from shape deep pose estimation for arbitrary 3d objects"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "densereg fully convolutional dense shape regression in the wild"
    - "3d pose estimation and 3d model retrieval for objects in the wild"
    - "a mixed classification regression framework for 3d pose estimation from 2d images"
    - "matching rgb images to cad models for object pose estimation"
    - "atlasnet a papier m ach e approach to learning 3d surface generation"
    - "dynamic graph cnn for learning on point clouds"
    - "making deep heatmaps robust to partial occlusions for 3d object pose estimation"

Connected through hyperedge HE-6 (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"
    - "deep directional statistics pose estimation with uncertainty quantification"
    - "click here human localized keypoints as guidance for viewpoint estimation"

Connected through hyperedge HE-7 (induced by source paper "joint viewpoint and keypoint estimation with real and synthetic data"):
  with members:
    - "3d pose estimation and 3d model retrieval for objects in the wild"
    - "viewpoint estimation insights model"
    - "3d pose estimation for fine grained object categories"
    - "learning to estimate 3d hand pose from single rgb images"
    - "ssd 6d making rgb based 3d detection and 6d pose estimation great again"
    - "6 dof object pose from semantic keypoints"
    - "multi context attention for human pose estimation"
    - "convolutional pose machines"

Connected through hyperedge HE-8 (induced by source paper "learning a disentangled embedding for monocular 3d shape retrieval and pose estimation"):
  with members:
    - "monocular object orientation estimation using riemannian regression and classification networks"
    - "densereg fully convolutional dense shape regression in the wild"
    - "3d pose estimation and 3d model retrieval for objects in the wild"
    - "a mixed classification regression framework for 3d pose estimation from 2d images"
    - "on the continuity of rotation representations in neural networks"
    - "text2shape generating shapes from natural language by learning joint embeddings"
    - "a unified framework for multi view multi class object pose estimation"
    - "3d pose regression using convolutional neural 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)