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: "auxnet auxiliary tasks enhanced semantic segmentation for automated driving"
  Abstract: Decision making in automated driving is highly specific to the environment and thus semantic segmentation plays a key role in recognizing the objects in the environment around the car. Pixel level classification once considered a challenging task which is now becoming mature to be productized in a car. However, semantic annotation is time consuming and quite expensive. Synthetic datasets with domain adaptation techniques have been used to alleviate the lack of large annotated datasets. In this work, we explore an alternate approach of leveraging the annotations of other tasks to improve semantic segmentation. Recently, multi-task learning became a popular paradigm in automated driving which demonstrates joint learning of multiple tasks improves overall performance of each tasks. Motivated by this, we use auxiliary tasks like depth estimation to improve the performance of semantic segmentation task. We propose adaptive task loss weighting techniques to address scale issues in multi-task loss functions which become more crucial in auxiliary tasks. We experimented on automotive datasets including SYNTHIA and KITTI and obtained 3% and 5% improvement in accuracy respectively.

Connected through hyperedge HE-1 (induced by source paper "multidepth single image depth estimation via multi task regression and classification"):
  with members:
    - "deep attention based classification network for robust depth prediction"
    - "on the importance of stereo for accurate depth estimation an efficient semi supervised deep neural network approach"
    - "planenet piece wise planar reconstruction from a single rgb image"
    - "segstereo exploiting semantic information for disparity estimation"
    - "deep ordinal regression network for monocular depth estimation"
    - "a modulation module for multi task learning with applications in image retrieval"
    - "depth estimation via affinity learned with convolutional spatial propagation network"
    - "learning monocular depth by distilling cross domain stereo networks"

Connected through hyperedge HE-2 (induced by source paper "multinet multi stream feature aggregation and geometric loss strategy for multi task learning"):
  with members:
    - "multi stream cnn based video semantic segmentation for automated driving"
    - "neurall towards a unified model for visual perception in automated driving"
    - "a hierarchical multi task approach for learning embeddings from semantic tasks"
    - "end to end multi task learning with attention"
    - "multi task learning as multi objective optimization"
    - "digrad multi task reinforcement learning with shared actions"
    - "universal representations the missing link between faces text planktons and cat breeds"
    - "flow guided feature aggregation for video object detection"

Connected through hyperedge HE-3 (induced by source paper "woodscape a multi task multi camera fisheye dataset for autonomous driving"):
  with members:
    - "yes we gan applying adversarial techniques for autonomous driving"
    - "neurall towards a unified model for visual perception in automated driving"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "less is more culling the training set to improve robustness of deep neural networks"
    - "deep learning is robust to massive label noise"
    - "rtseg real time semantic segmentation comparative study"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "an efficient optical flow based motion detection method for non stationary scenes"

Connected through hyperedge HE-4 (induced by source paper "fisheyemultinet real time multi task learning architecture for surround view automated parking system"):
  with members:
    - "multinet multi stream feature aggregation and geometric loss strategy for multi task learning"
    - "woodscape a multi task multi camera fisheye dataset for autonomous driving"
    - "neurall towards a unified model for visual perception in automated driving"
    - "soilingnet soiling detection on automotive surround view cameras"
    - "rtseg real time semantic segmentation comparative study"
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "gradnorm gradient normalization for adaptive loss balancing in deep multitask networks"
    - "you only look once unified real time object detection"

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)