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: "multiclass weighted loss for instance segmentation of cluttered cells"
  Abstract: We propose a new multiclass weighted loss function for instance segmentation of cluttered cells. We are primarily motivated by the need of developmental biologists to quantify and model the behavior of blood T -cells which might help us in understanding their regulation mechanisms and ultimately help researchers in their quest for developing an effective immunotherapy cancer treatment. Segmenting individual touching cells in cluttered regions is challenging as the feature distribution on shared borders and cell foreground are similar thus difficulting discriminating pixels into proper classes. We present two novel weight maps applied to the weighted cross entropy loss function which take into account both class imbalance and cell geometry. Binary ground truth training data is augmented so the learning model can handle not only foreground and background but also a third touching class. This framework allows training using U - N et. Experiments with our formulations have shown superior results when compared to other similar schemes, outperforming binary class models with significant improvement of boundary adequacy and instance detection. We validate our results on manually annotated microscope images of T -cells.

Connected through hyperedge HE-1 (induced by source paper "an auxiliary task for learning nuclei segmentation in 3d microscopy images"):
  with members:
    - "star convex polyhedra for 3d object detection and segmentation in microscopy"
    - "synaptic cleft segmentation in non isotropic volume electron microscopy of the complete drosophila brain"
    - "deep learning based instance segmentation in 3d biomedical images using weak annotation"
    - "deep watershed transform for instance segmentation"
    - "dcan deep contour aware networks for accurate gland segmentation"
    - "u net convolutional networks for biomedical image segmentation"
    - "adam a method for stochastic optimization"
    - "mahotas open source software for scriptable computer vision"

Connected through hyperedge HE-2 (induced by source paper "a multi task u net for segmentation with lazy labels"):
  with members:
    - "ficklenet weakly and semi supervised semantic image segmentation using stochastic inference"
    - "joint weakly and semi supervised deep learning for localization and classification of masses in breast ultrasound images"
    - "coarse to fine semantic segmentation from image level labels"
    - "a two stage 3d unet framework for multi class segmentation on full resolution image"
    - "deep learning with mixed supervision for brain tumor segmentation"
    - "coarse to fine volumetric segmentation of teeth in cone beam ct"
    - "revisiting dilated convolution a simple approach for weakly and semi supervised semantic segmentation"
    - "saliency guided deep network for weakly supervised image segmentation"

Connected through hyperedge HE-3 (induced by source paper "j regularization improves imbalanced multiclass segmentation"):
  with members:
    - "panoptic segmentation"
    - "cnn based preprocessing to optimize watershed based cell segmentation in 3d confocal microscopy images"
    - "visualizing the loss landscape of neural nets"
    - "generalised dice overlap as a deep learning loss function for highly unbalanced segmentations"
    - "tversky loss function for image segmentation using 3d fully convolutional deep networks"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"
    - "u net convolutional networks for biomedical image segmentation"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "a weakly supervised method for instance segmentation of biological cells"):
  with members:
    - "panoptic segmentation"
    - "constrained cnn losses for weakly supervised segmentation"
    - "suggestive annotation a deep active learning framework for biomedical image segmentation"
    - "mask r cnn"
    - "deep watershed transform for instance segmentation"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-5 (induced by source paper "cell detection with star convex polygons"):
  with members:
    - "arbitrary oriented scene text detection via rotation proposals"
    - "mask r cnn"
    - "ssd single shot multibox detector"
    - "parsing images of overlapping organisms with deep singling out networks"
    - "dcan deep contour aware networks for accurate gland segmentation"
    - "moral lineage tracing"
    - "straight to shapes real time detection of encoded shapes"
    - "you only look once unified real time object detection"

Connected through hyperedge HE-6 (induced by source paper "cnn based preprocessing to optimize watershed based cell segmentation in 3d confocal microscopy images"):
  with members:
    - "learned watershed end to end learning of seeded segmentation"
    - "cell segmentation in 3d confocal images using supervoxel merge forests with cnn based hypothesis selection"
    - "a deep structured learning approach towards automating connectome reconstruction from 3d electron micrographs"
    - "segment3d a web based application for collaborative segmentation of 3d images used in the shoot apical meristem"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "deep watershed transform for instance segmentation"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-7 (induced by source paper "u net for mav based penstock inspection an investigation of focal loss in multi class segmentation for corrosion identification"):
  with members:
    - "a systematic study of the class imbalance problem in convolutional neural networks"
    - "synthesizing training data for object detection in indoor scenes"
    - "u net convolutional networks for biomedical image segmentation"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"
    - "fully convolutional networks for semantic segmentation"
    - "pedestrian detection with unsupervised multi stage feature 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)