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: "analyzing modular cnn architectures for joint depth prediction and semantic segmentation"
  Abstract: This paper addresses the task of designing a modular neural network architecture that jointly solves different tasks. As an example we use the tasks of depth estimation and semantic segmentation given a single RGB image. The main focus of this work is to analyze the cross-modality influence between depth and semantic prediction maps on their joint refinement. While most of the previous works solely focus on measuring improvements in accuracy, we propose a way to quantify the cross-modality influence. We show that there is a relationship between final accuracy and cross-modality influence, although not a simple linear one. Hence a larger cross-modality influence does not necessarily translate into an improved accuracy. We find that a beneficial balance between the cross-modality influences can be achieved by network architecture and conjecture that this relationship can be utilized to understand different network design choices. Towards this end we propose a Convolutional Neural Network (CNN) architecture that fuses the state-of-the-art results for depth estimation and semantic labeling. By balancing the cross-modality influences between depth and semantic prediction, we achieve improved results for both tasks using the NYU-Depth v2 benchmark.

Connected through hyperedge HE-1 (induced by source paper "um adapt unsupervised multi task adaptation using adversarial cross task distillation"):
  with members:
    - "learning to adapt structured output space for semantic segmentation"
    - "adadepth unsupervised content congruent adaptation for depth estimation"
    - "end to end multi task learning with attention"
    - "image to image translation for domain adaptation"
    - "domain adaptation for visual applications a comprehensive survey"
    - "no more discrimination cross city adaptation of road scene segmenters"
    - "cycada cycle consistent adversarial domain adaptation"
    - "unsupervised learning of depth and ego motion from video"

Connected through hyperedge HE-2 (induced by source paper "auxnet auxiliary tasks enhanced semantic segmentation for automated driving"):
  with members:
    - "effnet an efficient structure for convolutional neural networks"
    - "monocular fisheye camera depth estimation using sparse lidar supervision"
    - "auxiliary tasks in multi task learning"
    - "driving scene perception network real time joint detection depth estimation and semantic segmentation"
    - "end to end multi task learning with attention"
    - "multi task learning as multi objective optimization"
    - "monocular depth estimation by learning from heterogeneous datasets"
    - "synscapes a photorealistic synthetic dataset for street scene parsing"

Connected through hyperedge HE-3 (induced by source paper "the rgb d triathlon towards agile visual toolboxes for robots"):
  with members:
    - "the limits and potentials of deep learning for robotics"
    - "adding new tasks to a single network with weight transformations using binary masks"
    - "taskonomy disentangling task transfer learning"
    - "rgb d object detection and semantic segmentation for autonomous manipulation in clutter"
    - "piggyback adapting a single network to multiple tasks by learning to mask weights"
    - "depth cnns for rgb d scene recognition learning from scratch better than transferring from rgb cnns"
    - "3d pose regression using convolutional neural networks"
    - "packnet adding multiple tasks to a single network by iterative pruning"

Connected through hyperedge HE-4 (induced by source paper "learning to reconstruct and understand indoor scenes from sparse views"):
  with members:
    - "deepmvs learning multi view stereopsis"
    - "structured attention guided convolutional neural fields for monocular depth estimation"
    - "pad net multi tasks guided prediction and distillation network for simultaneous depth estimation and scene parsing"
    - "mvsnet depth inference for unstructured multi view stereo"
    - "recurrent scene parsing with perspective understanding in the loop"
    - "multi scale continuous crfs as sequential deep networks for monocular depth estimation"
    - "matterport3d learning from rgb d data in indoor environments"
    - "multi view deep learning for consistent semantic mapping with rgb d cameras"

Connected through hyperedge HE-5 (induced by source paper "pad net multi tasks guided prediction and distillation network for simultaneous depth estimation and scene parsing"):
  with members:
    - "structured attention guided convolutional neural fields for monocular depth estimation"
    - "learning deep structured multi scale features using attention gated crfs for contour prediction"
    - "multi task learning using uncertainty to weigh losses for scene geometry and semantics"
    - "recurrent scene parsing with perspective understanding in the loop"
    - "multi scale continuous crfs as sequential deep networks for monocular depth estimation"
    - "monocular depth estimation with hierarchical fusion of dilated cnns and soft weighted sum inference"
    - "heterogeneous face attribute estimation a deep multi task learning approach"
    - "the cityscapes dataset for semantic urban scene understanding"

Connected through hyperedge HE-6 (induced by source paper "learning semantic segmentation from synthetic data a geometrically guided input output adaptation approach"):
  with members:
    - "penalizing top performers conservative loss for semantic segmentation adaptation"
    - "learning to adapt structured output space for semantic segmentation"
    - "domain adaptive faster r cnn for object detection in the wild"
    - "pad net multi tasks guided prediction and distillation network for simultaneous depth estimation and scene parsing"
    - "dlow domain flow for adaptation and generalization"
    - "spigan privileged adversarial learning from simulation"
    - "image to image translation for domain adaptation"
    - "domain adaptation for visual applications a comprehensive survey"

Connected through hyperedge HE-7 (induced by source paper "geo supervised visual depth prediction"):
  with members:
    - "visual inertial object detection and mapping"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "planenet piece wise planar reconstruction from a single rgb image"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "unsupervised learning of depth and ego motion from video"
    - "learning depth from monocular videos using direct methods"
    - "optnet differentiable optimization as a layer in neural networks"
    - "the cityscapes dataset for semantic urban scene understanding"

Connected through hyperedge HE-8 (induced by source paper "gaple generalizable approaching policy learning for robotic object searching in indoor environment"):
  with members:
    - "deep ordinal regression network for monocular depth estimation"
    - "building generalizable agents with a realistic and rich 3d environment"
    - "driving policy transfer via modularity and abstraction"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "active object perceiver recognition guided policy learning for object searching on mobile robots"
    - "visual representations for semantic target driven navigation"
    - "a dataset for developing and benchmarking active vision"
    - "divide and conquer reinforcement learning"

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)