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: "direction aware spatial context features for shadow detection"
  Abstract: Shadow detection is a fundamental and challenging task, since it requires an understanding of global image semantics and there are various backgrounds around shadows. This paper presents a novel network for shadow detection by analyzing image context in a direction-aware manner. To achieve this, we first formulate the direction-aware attention mechanism in a spatial recurrent neural network (RNN) by introducing attention weights when aggregating spatial context features in the RNN. By learning these weights through training, we can recover direction-aware spatial context (DSC) for detecting shadows. This design is developed into the DSC module and embedded in a CNN to learn DSC features at different levels. Moreover, a weighted cross entropy loss is designed to make the training more effective. We employ two common shadow detection benchmark datasets and perform various experiments to evaluate our network. Experimental results show that our network outperforms state-of-the-art methods and achieves 97% accuracy and 38% reduction on balance error rate.

Connected through hyperedge HE-1 (induced by source paper "region refinement network for salient object detection"):
  with members:
    - "se2net siamese edge enhancement network for salient object detection"
    - "a simple pooling based design for real time salient object detection"
    - "dna deeply supervised nonlinear aggregation for salient object detection"
    - "cascaded partial decoder for fast and accurate salient object detection"
    - "salient object detection a survey"
    - "deeply supervised salient object detection with short connections"
    - "selectivity or invariance boundary aware salient object detection"
    - "revisiting salient object detection simultaneous detection ranking and subitizing of multiple salient objects"

Connected through hyperedge HE-2 (induced by source paper "sac net spatial attenuation context for salient object detection"):
  with members:
    - "salient object detection with lossless feature reflection and weighted structural loss"
    - "pyramid feature attention network for saliency detection"
    - "a simple pooling based design for real time salient object detection"
    - "cascaded partial decoder for fast and accurate salient object detection"
    - "deeply supervised salient object detection with short connections"
    - "group normalization"
    - "reverse attention for salient object detection"
    - "direction aware spatial context features for shadow detection and removal"

Connected through hyperedge HE-3 (induced by source paper "where is my mirror"):
  with members:
    - "deeply supervised salient object detection with short connections"
    - "salient objects in clutter bringing salient object detection to the foreground"
    - "path aggregation network for instance segmentation"
    - "reverse attention for salient object detection"
    - "cbam convolutional block attention module"
    - "context encoding for semantic segmentation"
    - "a d net training a shadow detector with adversarial shadow attenuation"
    - "picanet learning pixel wise contextual attention for saliency detection"

Connected through hyperedge HE-4 (induced by source paper "argan attentive recurrent generative adversarial network for shadow detection and removal"):
  with members:
    - "learning intrinsic image decomposition from watching the world"
    - "dynamic meta embeddings for improved sentence representations"
    - "spectral normalization for generative adversarial networks"
    - "end to end active object tracking and its real world deployment via reinforcement learning"
    - "direction aware spatial context features for shadow detection and removal"
    - "a d net training a shadow detector with adversarial shadow attenuation"
    - "attentive generative adversarial network for raindrop removal from a single image"
    - "unsupervised image to image translation networks"

Connected through hyperedge HE-5 (induced by source paper "mask shadowgan learning to remove shadows from unpaired data"):
  with members:
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "direction aware spatial context features for shadow detection and removal"
    - "drit diverse image to image translation via disentangled representations"
    - "a d net training a shadow detector with adversarial shadow attenuation"
    - "transitive invariance for self supervised visual representation learning"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "stacked conditional generative adversarial networks for jointly learning shadow detection and shadow removal"

Connected through hyperedge HE-6 (induced by source paper "spatial attentive single image deraining with a high quality real rain dataset"):
  with members:
    - "residual guide feature fusion network for single image deraining"
    - "recurrent squeeze and excitation context aggregation net for single image deraining"
    - "non locally enhanced encoder decoder network for single image de raining"
    - "robust video content alignment and compensation for rain removal in a cnn framework"
    - "density aware single image de raining using a multi stream dense network"
    - "renoir a dataset for real low light image noise reduction"
    - "vital visual tracking via adversarial learning"
    - "what can help pedestrian detection"

Connected through hyperedge HE-7 (induced by source paper "direction aware spatial context features for shadow detection and removal"):
  with members:
    - "rendering synthetic objects into legacy photographs"
    - "intrinsic image transformation via scale space decomposition"
    - "fast shadow detection from a single image using a patched convolutional neural network"
    - "amulet aggregating multi level convolutional features for salient object detection"
    - "stacked conditional generative adversarial networks for jointly learning shadow detection and shadow removal"
    - "training region based object detectors with online hard example mining"
    - "pyramid scene parsing network"
    - "unified depth prediction and intrinsic image decomposition from a single image via joint convolutional neural fields"

Connected through hyperedge HE-8 (induced by source paper "optimizing the f measure for threshold free salient object detection"):
  with members:
    - "res2net a new multi scale backbone architecture"
    - "a simple pooling based design for real time salient object detection"
    - "salient object detection a survey"
    - "deeply supervised salient object detection with short connections"
    - "egnet edge guidance network for salient object detection"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "salient objects in clutter bringing salient object detection to the foreground"
    - "hi fi hierarchical feature integration for skeleton 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)