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: "semantic segmentation of video sequences with convolutional lstms"
  Abstract: Most of the semantic segmentation approaches have been developed for single image segmentation, and hence, video sequences are currently segmented by processing each frame of the video sequence separately. The disadvantage of this is that temporal image information is not considered, which improves the performance of the segmentation approach. One possibility to include temporal information is to use recurrent neural networks. However, there are only a few approaches using recurrent networks for video segmentation so far. These approaches extend the encoder-decoder network architecture of well-known segmentation approaches and place convolutional LSTM layers between encoder and decoder. However, in this paper it is shown that this position is not optimal, and that other positions in the network exhibit better performance. Nowadays, state-of-the-art segmentation approaches rarely use the classical encoder-decoder structure, but use multi-branch architectures. These architectures are more complex, and hence, it is more difficult to place the recurrent units at a proper position. In this work, the multi-branch architectures are extended by convolutional LSTM layers at different positions and evaluated on two different datasets in order to find the best one. It turned out that the proposed approach outperforms the pure CNN-based approach for up to 1.6 percent.

Connected through hyperedge HE-1 (induced by source paper "a survey on deep learning based architectures for semantic segmentation on 2d images"):
  with members:
    - "panoptic segmentation"
    - "expectation maximization attention networks for semantic segmentation"
    - "yolact real time instance segmentation"
    - "convolutional crfs for semantic segmentation"
    - "learning a discriminative feature network for semantic segmentation"
    - "semantic video segmentation a review on recent approaches"
    - "attention u net learning where to look for the pancreas"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"

Connected through hyperedge HE-2 (induced by source paper "robust semantic segmentation in adverse weather conditions by means of sensor data fusion"):
  with members:
    - "i can see clearly now image restoration via de raining"
    - "semantic foggy scene understanding with synthetic data"
    - "optimal sensor data fusion architecture for object detection in adverse weather conditions"
    - "boosting lidar based semantic labeling by cross modal training data generation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "joint 3d proposal generation and object detection from view aggregation"
    - "deep semantic classification for 3d lidar data"
    - "fast lidar based road detection using fully convolutional neural networks"

Connected through hyperedge HE-3 (induced by source paper "separable convolutional lstms for faster video segmentation"):
  with members:
    - "robust semantic segmentation in adverse weather conditions by means of sensor data fusion"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "factorization tricks for lstm networks"
    - "icnet for real time semantic segmentation on high resolution images"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "virtual worlds as proxy for multi object tracking analysis"
    - "recurrent fully convolutional networks for video 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)