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 depth estimation a survey"
  Abstract: Monocular depth estimation is often described as an ill-posed and inherently ambiguous problem. Estimating depth from 2D images is a crucial step in scene reconstruction, 3Dobject recognition, segmentation, and detection. The problem can be framed as: given a single RGB image as input, predict a dense depth map for each pixel. This problem is worsened by the fact that most scenes have large texture and structural variations, object occlusions, and rich geometric detailing. All these factors contribute to difficulty in accurate depth estimation. In this paper, we review five papers that attempt to solve the depth estimation problem with various techniques including supervised, weakly-supervised, and unsupervised learning techniques. We then compare these papers and understand the improvements made over one another. Finally, we explore potential improvements that can aid to better solve this problem.

Connected through hyperedge HE-1 (induced by source paper "close proximity underwater terrain mapping using learning based coarse range estimation"):
  with members:
    - "inferring distributions over depth from a single image"
    - "deep ordinal regression network for monocular depth estimation"
    - "bonnet an open source training and deployment framework for semantic segmentation in robotics using cnns"
    - "focal loss for dense object detection"
    - "airsim high fidelity visual and physical simulation for autonomous vehicles"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "deeper depth prediction with fully convolutional residual networks"
    - "estimating depth from monocular images as classification using deep fully convolutional residual networks"

Connected through hyperedge HE-2 (induced by source paper "low viewpoint forest depth dataset for sparse rover swarms"):
  with members:
    - "structured attention guided convolutional neural fields for monocular depth estimation"
    - "high quality monocular depth estimation via transfer learning"
    - "rgbd datasets past present and future"
    - "deeper depth prediction with fully convolutional residual networks"
    - "depth map prediction from a single image using a multi scale deep network"

Connected through hyperedge HE-3 (induced by source paper "monster awakening the mono in stereo"):
  with members:
    - "single image depth estimation trained via depth from defocus cues"
    - "deep optics for monocular depth estimation and 3d object detection"
    - "ga net guided aggregation net for end to end stereo matching"
    - "learning depth with convolutional spatial propagation network"
    - "deep depth from defocus how can defocus blur improve 3d estimation using dense neural networks"
    - "hierarchical discrete distribution decomposition for match density estimation"
    - "pyramid stereo matching network"
    - "deep depth inference using binocular and monocular cues"

Connected through hyperedge HE-4 (induced by source paper "a robust billboard based free viewpoint video synthesizing algorithm for sports scenes"):
  with members:
    - "deep ordinal regression network for monocular depth estimation"
    - "monocular depth estimation using multi scale continuous crfs as sequential deep networks"
    - "soccer on your tabletop"
    - "towards 3d human pose estimation in the wild a weakly supervised approach"
    - "lifting from the deep convolutional 3d pose estimation from a single image"
    - "rethinking atrous convolution for semantic image segmentation"
    - "convolutional pose machines"
    - "coarse to fine volumetric prediction for single image 3d human pose"

Connected through hyperedge HE-5 (induced by source paper "unrealrox an extremely photorealistic virtual reality environment for robotics simulations and synthetic data generation"):
  with members:
    - "learning to augment synthetic images for sim2real policy transfer"
    - "gibson env real world perception for embodied agents"
    - "chalet cornell house agent learning environment"
    - "structured attention guided convolutional neural fields for monocular depth estimation"
    - "training deep networks with synthetic data bridging the reality gap by domain randomization"
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"
    - "minos multimodal indoor simulator for navigation in complex environments"
    - "home a household multimodal environment"

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)