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: "incremental learning for semantic segmentation of large scale remote sensing data"
  Abstract: In spite of remarkable success of the convolutional neural networks on semantic segmentation, they suffer from catastrophic forgetting: a significant performance drop for the already learned classes when new classes are added on the data, having no annotations for the old classes. We propose an incremental learning methodology, enabling to learn segmenting new classes without hindering dense labeling abilities for the previous classes, although the entire previous data are not accessible. The key points of the proposed approach are adapting the network to learn new as well as old classes on the new training data, and allowing it to remember the previously learned information for the old classes. For adaptation, we keep a frozen copy of the previously trained network, which is used as a memory for the updated network in absence of annotations for the former classes. The updated network minimizes a loss function, which balances the discrepancy between outputs for the previous classes from the memory and updated networks, and the mis-classification rate between outputs for the new classes from the updated network and the new ground-truth. For remembering, we either regularly feed samples from the stored, little fraction of the previous data or use the memory network, depending on whether the new data are collected from completely different geographic areas or from the same city. Our experimental results prove that it is possible to add new classes to the network, while maintaining its performance for the previous classes, despite the whole previous training data are not available.

Connected through hyperedge HE-1 (induced by source paper "modeling the background for incremental learning in semantic segmentation"):
  with members:
    - "incremental learning techniques for semantic segmentation"
    - "large scale incremental learning"
    - "continual learning a comparative study on how to defy forgetting in classification tasks"
    - "learning to remember a synaptic plasticity driven framework for continual learning"
    - "learning without memorizing"
    - "learn the new keep the old extending pretrained models with new anatomy and images"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "riemannian walk for incremental learning understanding forgetting and intransigence"

Connected through hyperedge HE-2 (induced by source paper "dissecting catastrophic forgetting in continual learning by deep visualization"):
  with members:
    - "incremental learning techniques for semantic segmentation"
    - "contcap a comprehensive framework for continual image captioning"
    - "a unified framework for lifelong learning in deep neural networks"
    - "toward understanding catastrophic forgetting in continual learning"
    - "lifelong gan continual learning for conditional image generation"
    - "an empirical study of example forgetting during deep neural network learning"
    - "progressive neural networks"
    - "continual learning through synaptic intelligence"

Connected through hyperedge HE-3 (induced by source paper "colormapgan unsupervised domain adaptation for semantic segmentation using color mapping generative adversarial networks"):
  with members:
    - "unsupervised domain adaptation using generative adversarial networks for semantic segmentation of aerial images"
    - "active multi kernel domain adaptation for hyperspectral image classification"
    - "penalizing top performers conservative loss for semantic segmentation adaptation"
    - "learning to adapt structured output space for semantic segmentation"
    - "multimodal unsupervised image to image translation"
    - "a domain agnostic normalization layer for unsupervised adversarial domain adaptation"
    - "ternausnetv2 fully convolutional network for instance segmentation"
    - "ternausnet u net with vgg11 encoder pre trained on imagenet for image segmentation"

Connected through hyperedge HE-4 (induced by source paper "knowledge distillation for incremental learning in semantic segmentation"):
  with members:
    - "incremental learning techniques for semantic segmentation"
    - "recent progress in semantic image segmentation"
    - "large scale incremental learning"
    - "m2kd multi model and multi level knowledge distillation for incremental learning"
    - "adversarial learning and self teaching techniques for domain adaptation in semantic segmentation"
    - "similarity preserving knowledge distillation"
    - "learning without memorizing"
    - "incremental classifier learning with generative adversarial networks"

Connected through hyperedge HE-5 (induced by source paper "incremental learning techniques for semantic segmentation"):
  with members:
    - "large scale incremental learning"
    - "m2kd multi model and multi level knowledge distillation for incremental learning"
    - "tree cnn a hierarchical deep convolutional neural network for incremental learning"
    - "learning without memorizing"
    - "incremental classifier learning with generative adversarial networks"
    - "tree cnn a deep convolutional neural network for lifelong learning"
    - "incremental training of deep convolutional neural networks"
    - "game theoretic analysis of road user safety scenarios involving autonomous vehicles"

Connected through hyperedge HE-6 (induced by source paper "contcap a comprehensive framework for continual image captioning"):
  with members:
    - "fine grained continual learning"
    - "incremental learning techniques for semantic segmentation"
    - "incremental classifier learning with generative adversarial networks"
    - "neural baby talk"
    - "continuous learning in single incremental task scenarios"
    - "show control and tell a framework for generating controllable and grounded captions"
    - "end to end incremental learning"
    - "progressive neural networks"

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.LG(Machine Learning)