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: "monocular object orientation estimation using riemannian regression and classification networks"
  Abstract: We consider the task of estimating the 3D orientation of an object of known category given an image of the object and a bounding box around it. Recently, CNN-based regression and classification methods have shown significant performance improvements for this task. This paper proposes a new CNN-based approach to monocular orientation estimation that advances the state of the art in four different directions. First, we take into account the Riemannian structure of the orientation space when designing regression losses and nonlinear activation functions. Second, we propose a mixed Riemannian regression and classification framework that better handles the challenging case of nearly symmetric objects. Third, we propose a data augmentation strategy that is specifically designed to capture changes in 3D orientation. Fourth, our approach leads to state-of-the-art results on the PASCAL3D+ dataset.

Connected through hyperedge HE-1 (induced by source paper "the costar block stacking dataset learning with workspace constraints"):
  with members:
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "playing hard exploration games by watching youtube"
    - "a real time robotic grasp approach with oriented anchor box"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "robustness via retrying closed loop robotic manipulation with self supervised learning"
    - "sim to real via sim to sim data efficient robotic grasping via randomized to canonical adaptation networks"
    - "shapestacks learning vision based physical intuition for generalised object stacking"
    - "evaluating methods for end user creation of robot task plans"

Connected through hyperedge HE-2 (induced by source paper "learning a disentangled embedding for monocular 3d shape retrieval and pose estimation"):
  with members:
    - "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"
    - "starmap for category agnostic keypoint and viewpoint estimation"
    - "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)