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: "microscopy cell segmentation via convolutional lstm networks"
  Abstract: Live cell microscopy sequences exhibit complex spatial structures and complicated temporal behaviour, making their analysis a challenging task. Considering cell segmentation problem, which plays a significant role in the analysis, the spatial properties of the data can be captured using Convolutional Neural Networks (CNNs). Recent approaches show promising segmentation results using convolutional encoder-decoders such as the U-Net. Nevertheless, these methods are limited by their inability to incorporate temporal information, that can facilitate segmentation of individual touching cells or of cells that are partially visible. In order to accommodate cell dynamics we propose a novel segmentation approach which integrates Convolutional Long Short Term Memory (C-LSTM) with the U-Net. The network's unique architecture allows it to capture multi-scale, compact, spatio-temporal encoding in the C-LSTMs memory units. Promising results, surpassing the state-of-the-art, are presented. The code is freely available at: TBD

Connected through hyperedge HE-1 (induced by source paper "mpm joint representation of motion and position map for cell tracking"):
  with members:
    - "joint multi frame detection and segmentation for multi cell tracking"
    - "instance segmentation and tracking with cosine embeddings and recurrent hourglass networks"
    - "end to end representation learning for correlation filter based tracking"
    - "tracking the untrackable learning to track multiple cues with long term dependencies"
    - "ubernet training a universal convolutional neural network for low mid and high level vision using diverse datasets and limited memory"
    - "cross stitch networks for multi task learning"
    - "poi multiple object tracking with high performance detection and appearance feature"
    - "r cnns for pose estimation and action detection"

Connected through hyperedge HE-2 (induced by source paper "joint multi frame detection and segmentation for multi cell tracking"):
  with members:
    - "unet a nested u net architecture for medical image segmentation"
    - "instance segmentation and tracking with cosine embeddings and recurrent hourglass networks"
    - "microscopy cell segmentation via adversarial neural networks"
    - "tracking the untrackable learning to track multiple cues with long term dependencies"
    - "stacked hourglass networks for human pose estimation"
    - "u net convolutional networks for biomedical image segmentation"
    - "delving deeper into convolutional networks for learning video representations"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "qanet quality assurance network for image segmentation"):
  with members:
    - "yolov3 an incremental improvement"
    - "uncertainty in multitask learning joint representations for probabilistic mr only radiotherapy planning"
    - "towards safe deep learning accurately quantifying biomarker uncertainty in neural network predictions"
    - "microscopy cell segmentation via adversarial neural networks"
    - "yolo9000 better faster stronger"
    - "you only look once unified real time object detection"
    - "u net convolutional networks for biomedical image segmentation"

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)