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: "self supervised model adaptation for multimodal semantic segmentation"
  Abstract: Learning to reliably perceive and understand the scene is an integral enabler for robots to operate in the real-world. This problem is inherently challenging due to the multitude of object types as well as appearance changes caused by varying illumination and weather conditions. Leveraging complementary modalities can enable learning of semantically richer representations that are resilient to such perturbations. Despite the tremendous progress in recent years, most multimodal convolutional neural network approaches directly concatenate feature maps from individual modality streams rendering the model incapable of focusing only on the relevant complementary information for fusion. To address this limitation, we propose a mutimodal semantic segmentation framework that dynamically adapts the fusion of modality-specific features while being sensitive to the object category, spatial location and scene context in a self-supervised manner. Specifically, we propose an architecture consisting of two modality-specific encoder streams that fuse intermediate encoder representations into a single decoder using our proposed self-supervised model adaptation fusion mechanism which optimally combines complementary features. As intermediate representations are not aligned across modalities, we introduce an attention scheme for better correlation. In addition, we propose a computationally efficient unimodal segmentation architecture termed AdapNet++ that incorporates a new encoder with multiscale residual units and an efficient atrous spatial pyramid pooling that has a larger effective receptive field with more than $$10\,\times $$ fewer parameters, complemented with a strong decoder with a multi-resolution supervision scheme that recovers high-resolution details. Comprehensive empirical evaluations on Cityscapes, Synthia, SUN RGB-D, ScanNet and Freiburg Forest benchmarks demonstrate that both our unimodal and multimodal architectures achieve state-of-the-art performance while simultaneously being efficient in terms of parameters and inference time as well as demonstrating substantial robustness in adverse perceptual conditions.

Connected through hyperedge HE-1 (induced by source paper "sensor fusion gated recurrent fusion to learn driving behavior from temporal multimodal data"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "vlocnet deep multitask learning for semantic visual localization and odometry"
    - "online action detection in untrimmed streaming videos modeling and evaluation"
    - "learning to drive in a day"
    - "toward driving scene understanding a dataset for learning driver behavior and causal reasoning"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "lstm a search space odyssey"
    - "carla an open urban driving simulator"

Connected through hyperedge HE-2 (induced by source paper "gated path selection network for semantic segmentation"):
  with members:
    - "ocnet object context network for scene parsing"
    - "ccnet criss cross attention for semantic segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "deformable convnets v2 more deformable better results"
    - "deformable convolutional networks"
    - "understanding convolution for semantic segmentation"
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "rethinking atrous convolution for semantic image segmentation"

Connected through hyperedge HE-3 (induced by source paper "indoor scene recognition in 3d"):
  with members:
    - "4d spatio temporal convnets minkowski convolutional neural networks"
    - "associatively segmenting instances and semantics in point clouds"
    - "3d instance segmentation via multi task metric learning"
    - "learning effective rgb d representations for scene recognition"
    - "mesh r cnn"
    - "dynamic graph cnn for learning on point clouds"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "the kinetics human action video dataset"

Connected through hyperedge HE-4 (induced by source paper "self supervised object in gripper segmentation from robotic motions"):
  with members:
    - "towards segmenting everything that moves"
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "photorealistic image synthesis for object instance detection"
    - "deep neural network concepts for background subtraction a systematic review and comparative evaluation"
    - "learning instance segmentation by interaction"
    - "deep visual domain adaptation a survey"
    - "object discovery in videos as foreground motion clustering"
    - "fast online object tracking and segmentation a unifying approach"

Connected through hyperedge HE-5 (induced by source paper "virtual kitti 2"):
  with members:
    - "temporal coherence for active learning in videos"
    - "ga net guided aggregation net for end to end stereo matching"
    - "slanted stixels representing san francisco s steepest streets"
    - "unsupervised learning of depth and ego motion from video"
    - "playing for benchmarks"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "virtual worlds as proxy for multi object tracking analysis"
    - "feature pyramid networks for object detection"

Connected through hyperedge HE-6 (induced by source paper "two stream networks for self supervised ego motion estimation"):
  with members:
    - "beyond photometric loss for self supervised ego motion estimation"
    - "lo net deep real time lidar odometry"
    - "revisiting self supervised visual representation learning"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "df net unsupervised joint learning of depth and flow using cross task consistency"
    - "robustness meets deep learning an end to end hybrid pipeline for unsupervised learning of egomotion"
    - "vlocnet deep multitask learning for semantic visual localization and odometry"

Connected through hyperedge HE-7 (induced by source paper "dsnet an efficient cnn for road scene segmentation"):
  with members:
    - "espnet efficient spatial pyramid of dilated convolutions for semantic segmentation"
    - "contextnet exploring context and detail for semantic segmentation in real time"
    - "espnetv2 a light weight power efficient and general purpose convolutional neural network"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "searching for efficient multi scale architectures for dense image prediction"
    - "efficient dense modules of asymmetric convolution for real time semantic segmentation"
    - "icnet for real time semantic segmentation on high resolution images"
    - "enet a deep neural network architecture for real time semantic segmentation"

Connected through hyperedge HE-8 (induced by source paper "temporal multimodal fusion for driver behavior prediction tasks using gated recurrent fusion units"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "multimodal interaction aware motion prediction for autonomous street crossing"
    - "vlocnet deep multitask learning for semantic visual localization and odometry"
    - "online action detection in untrimmed streaming videos modeling and evaluation"
    - "learning to drive in a day"
    - "toward driving scene understanding a dataset for learning driver behavior and causal reasoning"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "carla an open urban driving simulator"

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)