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: "towards robust monocular depth estimation mixing datasets for zero shot cross dataset transfer"
  Abstract: The success of monocular depth estimation relies on large and diverse training sets. Due to the challenges associated with acquiring dense ground-truth depth across different environments at scale, a number of datasets with distinct characteristics and biases have emerged. We develop tools that enable mixing multiple datasets during training, even if their annotations are incompatible. In particular, we propose a training objective that is invariant to changes in depth range and scale. Armed with this objective, we explore an abundant source of training data: 3D films. We demonstrate that despite pervasive inaccuracies, 3D films constitute a useful source of data that is complementary to existing training sets. We evaluate the presented approach on diverse datasets, focusing on zero-shot cross-dataset transfer: testing the generality of the learned model by evaluating it on datasets that were not seen during training. The experiments confirm that mixing data from complementary sources yields improved depth estimates, particularly on previously unseen datasets. Some results are shown in the supplementary video: this https URL

Connected through hyperedge HE-1 (induced by source paper "depth estimation by learning triangulation and densification of sparse points for multi view stereo"):
  with members:
    - "from big to small multi scale local planar guidance for monocular depth estimation"
    - "multi view stereo by temporal nonparametric fusion"
    - "learnable triangulation of human pose"
    - "gradslam dense slam meets automatic differentiation"
    - "superglue learning feature matching with graph neural networks"
    - "efficientdet scalable and efficient object detection"
    - "dpsnet end to end deep plane sweep stereo"
    - "gift learning transformation invariant dense visual descriptors via group cnns"

Connected through hyperedge HE-2 (induced by source paper "learning depth via interaction"):
  with members:
    - "a 64 mw dnn based visual navigation engine for autonomous nano drones"
    - "confidence propagation through cnns for guided sparse depth regression"
    - "depth from videos in the wild unsupervised monocular depth learning from unknown cameras"
    - "deeptam deep tracking and mapping"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "unsupervised learning of shape and pose with differentiable point clouds"
    - "multi view consistency as supervisory signal for learning shape and pose prediction"

Connected through hyperedge HE-3 (induced by source paper "meshlet priors for 3d mesh reconstruction"):
  with members:
    - "photometric mesh optimization for video aligned 3d object reconstruction"
    - "multi chart generative surface modeling"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "what do single view 3d reconstruction networks learn"
    - "learning category specific mesh reconstruction from image collections"
    - "codeslam learning a compact optimisable representation for dense visual slam"
    - "deepmvs learning multi view stereopsis"
    - "learning 3d shape completion under weak supervision"

Connected through hyperedge HE-4 (induced by source paper "why do line drawings work a realism hypothesis"):
  with members:
    - "unsupervised doodling and painting with improved spiral"
    - "neural painters a learned differentiable constraint for generating brushstroke paintings"

Connected through hyperedge HE-5 (induced by source paper "regularization matters in policy optimization"):
  with members:
    - "investigating generalisation in continuous deep reinforcement learning"
    - "control regularization for reduced variance reinforcement learning"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "generalization and regularization in dqn"
    - "meta reinforcement learning of structured exploration strategies"
    - "a study on overfitting in deep reinforcement learning"
    - "structured control nets for deep reinforcement learning"
    - "quantifying generalization in reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "learning 3d aware egocentric spatial temporal interaction via graph convolutional networks"):
  with members:
    - "spatio temporal action graph networks"
    - "learning actor relation graphs for group activity recognition"
    - "intent aware multi agent reinforcement learning"
    - "traphic trajectory prediction in dense and heterogeneous traffic using weighted interactions"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "classifying collisions with spatio temporal action graph networks"
    - "semi supervised learning fusion of self supervised supervised learning and multimodal cues for tactical driver behavior detection"
    - "graph r cnn for scene graph generation"

Connected through hyperedge HE-7 (induced by source paper "diode a dense indoor and outdoor depth dataset"):
  with members:
    - "3d packing for self supervised monocular depth estimation"
    - "on the importance of stereo for accurate depth estimation an efficient semi supervised deep neural network approach"
    - "deep ordinal regression network for monocular depth estimation"
    - "megadepth learning single view depth prediction from internet photos"
    - "high quality monocular depth estimation via transfer learning"
    - "matterport3d learning from rgb d data in indoor environments"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "learning depth from single monocular images using deep convolutional neural fields"

Connected through hyperedge HE-8 (induced by source paper "in perfect shape certifiably optimal 3d shape reconstruction from 2d landmarks"):
  with members:
    - "a polynomial time solution for robust registration with extreme outlier rates"
    - "learning to reconstruct 3d human pose and shape via model fitting in the loop"
    - "convolutional mesh regression for single image human shape reconstruction"
    - "guaranteed globally optimal planar pose graph and landmark slam via sparse bounded sums of squares programming"
    - "modeling perceptual aliasing in slam via discrete continuous graphical models"
    - "sparse representation for 3d shape estimation a convex relaxation approach"
    - "lifting from the deep convolutional 3d pose estimation from a single image"
    - "posecnn a convolutional neural network for 6d object pose estimation in cluttered scenes"

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)