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: "combo loss handling input and output imbalance in multi organ segmentation"
  Abstract: Simultaneous segmentation of multiple organs from different medical imaging modalities is a crucial task as it can be utilized for computer-aided diagnosis, computer-assisted surgery, and therapy planning. Thanks to the recent advances in deep learning, several deep neural networks for medical image segmentation have been introduced successfully for this purpose. In this paper, we focus on learning a deep multi-organ segmentation network that labels voxels. In particular, we examine the critical choice of a loss function in order to handle the notorious imbalance problem that plagues both the input and output of a learning model. The input imbalance refers to the class-imbalance in the input training samples (i.e., small foreground objects embedded in an abundance of background voxels, as well as organs of varying sizes). The output imbalance refers to the imbalance between the false positives and false negatives of the inference model. In order to tackle both types of imbalance during training and inference, we introduce a new curriculum learning based loss function. Specifically, we leverage Dice similarity coefficient to deter model parameters from being held at bad local minima and at the same time gradually learn better model parameters by penalizing for false positives/negatives using a cross entropy term. We evaluated the proposed loss function on three datasets: whole body positron emission tomography (PET) scans with 5 target organs, magnetic resonance imaging (MRI) prostate scans, and ultrasound echocardigraphy images with a single target organ i.e., left ventricular. We show that a simple network architecture with the proposed integrative loss function can outperform state-of-the-art methods and results of the competing methods can be improved when our proposed loss is used.

Connected through hyperedge HE-1 (induced by source paper "recurrent convolutional neural networks for mandible segmentation from computed tomography"):
  with members:
    - "anatomynet deep learning for fast and fully automated whole volume segmentation of head and neck anatomy"
    - "organ at risk segmentation in head and neck ct images by using a two stage segmentation framework based on 3d u net"
    - "attention u net learning where to look for the pancreas"
    - "ischemic stroke lesion segmentation in ct perfusion scans using pyramid pooling and focal loss"
    - "image segmentation algorithms overview"
    - "mr based synthetic ct generation using a deep convolutional neural network method"
    - "generalised dice overlap as a deep learning loss function for highly unbalanced segmentations"
    - "recent trends in deep learning based natural language processing"

Connected through hyperedge HE-2 (induced by source paper "improved inference via deep input transfer"):
  with members:
    - "star shape prior in fully convolutional networks for skin lesion segmentation"
    - "a deep learning algorithm for one step contour aware nuclei segmentation of histopathological images"
    - "select attend and transfer light learnable skip connections"
    - "3d segmentation with exponential logarithmic loss for highly unbalanced object sizes"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "chest x ray analysis of tuberculosis by deep learning with segmentation and augmentation"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "a survey on deep learning in medical image analysis"

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)