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: "fast scene understanding for autonomous driving"
  Abstract: Most approaches for instance-aware semantic labeling traditionally focus on accuracy. Other aspects like runtime and memory footprint are arguably as important for real-time applications such as autonomous driving. Motivated by this observation and inspired by recent works that tackle multiple tasks with a single integrated architecture, in this paper we present a real-time efficient implementation based on ENet that solves three autonomous driving related tasks at once: semantic scene segmentation, instance segmentation and monocular depth estimation. Our approach builds upon a branched ENet architecture with a shared encoder but different decoder branches for each of the three tasks. The presented method can run at 21 fps at a resolution of 1024x512 on the Cityscapes dataset without sacrificing accuracy compared to running each task separately.

Connected through hyperedge HE-1 (induced by source paper "mti net multi scale task interaction networks for multi task learning"):
  with members:
    - "which tasks should be learned together in multi task learning"
    - "branched multi task networks deciding what layers to share"
    - "deep high resolution representation learning for visual recognition"
    - "pattern affinitive propagation across depth surface normal and semantic segmentation"
    - "deep high resolution representation learning for human pose estimation"
    - "panoptic feature pyramid networks"
    - "attentive single tasking of multiple tasks"
    - "a modulation module for multi task learning with applications in image retrieval"

Connected through hyperedge HE-2 (induced by source paper "dynamic task weighting methods for multi task networks in autonomous driving systems"):
  with members:
    - "multinet multi stream feature aggregation and geometric loss strategy for multi task learning"
    - "real time joint object detection and semantic segmentation network for automated driving"
    - "which tasks should be learned together in multi task learning"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "soilingnet soiling detection on automotive surround view cameras"
    - "taskonomy disentangling task transfer learning"
    - "autoaugment learning augmentation policies from data"
    - "piggyback adapting a single network to multiple tasks by learning to mask weights"

Connected through hyperedge HE-3 (induced by source paper "towards bounding box free panoptic segmentation"):
  with members:
    - "panoptic segmentation"
    - "devil is in the edges learning semantic boundaries from noisy annotations"
    - "panoptic edge detection"
    - "upsnet a unified panoptic segmentation network"
    - "fast panoptic segmentation network"
    - "adaptis adaptive instance selection network"
    - "ssap single shot instance segmentation with affinity pyramid"
    - "instance segmentation by jointly optimizing spatial embeddings and clustering bandwidth"

Connected through hyperedge HE-4 (induced by source paper "spatial sampling network for fast scene understanding"):
  with members:
    - "espnet efficient spatial pyramid of dilated convolutions for semantic segmentation"
    - "benchmark analysis of representative deep neural network architectures"
    - "guided upsampling network for real time semantic segmentation"
    - "contextnet exploring context and detail for semantic segmentation in real time"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "path aggregation network for instance segmentation"
    - "efficient dense modules of asymmetric convolution for real time semantic segmentation"
    - "multi task learning using uncertainty to weigh losses for scene geometry and semantics"

Connected through hyperedge HE-5 (induced by source paper "branched multi task networks deciding what layers to share"):
  with members:
    - "stochastic filter groups for multi task cnns learning specialist and generalist convolution kernels"
    - "representation similarity analysis for efficient task taxonomy transfer learning"
    - "feature partitioning for efficient multi task architectures"
    - "attentive single tasking of multiple tasks"
    - "a modulation module for multi task learning with applications in image retrieval"
    - "evolutionary architecture search for deep multitask networks"
    - "regularized evolution for image classifier architecture search"
    - "taskonomy disentangling task transfer learning"

Connected through hyperedge HE-6 (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-7 (induced by source paper "neurall towards a unified model for visual perception in automated driving"):
  with members:
    - "exploring applications of deep reinforcement learning for real world autonomous driving systems"
    - "model adaptation with synthetic and real data for semantic dense foggy scene understanding"
    - "yolov3 an incremental improvement"
    - "taskonomy disentangling task transfer learning"
    - "auxiliary tasks in multi task learning"
    - "learning efficient convolutional networks through network slimming"
    - "universal representations the missing link between faces text planktons and cat breeds"
    - "vinet visual inertial odometry as a sequence to sequence learning problem"

Connected through hyperedge HE-8 (induced by source paper "regularizing deep multi task networks using orthogonal gradients"):
  with members:
    - "meta learning representations for continual learning"
    - "which tasks should be learned together in multi task learning"
    - "on the variance of the adaptive learning rate and beyond"
    - "branched multi task networks deciding what layers to share"
    - "a modulation module for multi task learning with applications in image retrieval"
    - "taskonomy disentangling task transfer learning"
    - "end to end multi task learning with attention"
    - "multi task learning as multi objective optimization"

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)