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: "class incremental learning via deep model consolidation"
  Abstract: Deep neural networks (DNNs) often suffer from "catastrophic forgetting" during incremental learning (IL) --- an abrupt degradation of performance on the original set of classes when the training objective is adapted to a newly added set of classes. Existing IL approaches tend to produce a model that is biased towards either the old classes or new classes, unless with the help of exemplars of the old data. To address this issue, we propose a class-incremental learning paradigm called Deep Model Consolidation (DMC), which works well even when the original training data is not available. The idea is to first train a separate model only for the new classes, and then combine the two individual models trained on data of two distinct set of classes (old classes and new classes) via a novel dual distillation training objective. The two existing models are consolidated by exploiting publicly available unlabeled auxiliary data. This overcomes the potential difficulties due to unavailability of original training data. Compared to the state-of-the-art techniques, DMC demonstrates significantly better performance in CIFAR-100 image classification and PASCAL VOC 2007 object detection benchmarks in the single-headed IL setting.

Connected through hyperedge HE-1 (induced by source paper "ternary feature masks continual learning without any forgetting"):
  with members:
    - "learn to grow a continual structure learning framework for overcoming catastrophic forgetting"
    - "continual learning a comparative study on how to defy forgetting in classification tasks"
    - "rotate your networks better weight consolidation and less catastrophic forgetting"
    - "efficient lifelong learning with a gem"
    - "piggyback adapting a single network to multiple tasks by learning to mask weights"
    - "learning to learn without forgetting by maximizing transfer and minimizing interference"
    - "riemannian walk for incremental learning understanding forgetting and intransigence"
    - "overcoming catastrophic forgetting with hard attention to the task"

Connected through hyperedge HE-2 (induced by source paper "an adaptive random path selection approach for incremental learning"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "exploring randomly wired neural networks for image recognition"
    - "random path selection for incremental learning"
    - "re evaluating continual learning scenarios a categorization and case for strong baselines"
    - "deep generative dual memory network for continual learning"
    - "riemannian walk for incremental learning understanding forgetting and intransigence"
    - "generative replay with feedback connections as a general strategy for continual learning"
    - "efficient neural architecture search via parameter sharing"

Connected through hyperedge HE-3 (induced by source paper "continual learning in neural networks"):
  with members:
    - "knowledge transfer in deep block modular neural networks"
    - "meta learning representations for continual learning"
    - "continual reinforcement learning in 3d non stationary environments"
    - "continual learning with tiny episodic memories"
    - "continual classification learning using generative models"
    - "task free continual learning"
    - "re evaluating continual learning scenarios a categorization and case for strong baselines"
    - "experience replay for continual learning"

Connected through hyperedge HE-4 (induced by source paper "continual learning a comparative study on how to defy forgetting in classification tasks"):
  with members:
    - "continual learning for robotics"
    - "continual lifelong learning with neural networks a review"
    - "a comprehensive application oriented study of catastrophic forgetting in dnns"
    - "continual learning with tiny episodic memories"
    - "continual classification learning using generative models"
    - "task free continual learning"
    - "re evaluating continual learning scenarios a categorization and case for strong baselines"
    - "experience replay for continual learning"

Connected through hyperedge HE-5 (induced by source paper "single net continual learning with progressive segmented training pst"):
  with members:
    - "efficient network construction through structural plasticity"
    - "automatic compiler based fpga accelerator for cnn training"
    - "regularize expand and compress multi task based lifelong learning via nonexpansive automl"
    - "large scale incremental learning"
    - "towards efficient neural networks on a chip joint hardware algorithm approaches"
    - "revisiting distillation and incremental classifier learning"
    - "reinforced continual learning"
    - "piggyback adapting a single network to multiple tasks by learning to mask weights"

Connected through hyperedge HE-6 (induced by source paper "incremental concept learning via online generative memory recall"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "high fidelity image generation with fewer labels"
    - "incremental classifier learning with generative adversarial networks"
    - "revisiting distillation and incremental classifier learning"
    - "rotate your networks better weight consolidation and less catastrophic forgetting"
    - "spectral normalization for generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "memory replay gans learning to generate images from new categories without forgetting"

Connected through hyperedge HE-7 (induced by source paper "random path selection for incremental learning"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "exploring randomly wired neural networks for image recognition"
    - "re evaluating continual learning scenarios a categorization and case for strong baselines"
    - "deep generative dual memory network for continual learning"
    - "riemannian walk for incremental learning understanding forgetting and intransigence"
    - "generative replay with feedback connections as a general strategy for continual learning"
    - "efficient neural architecture search via parameter sharing"
    - "learning transferable architectures for scalable image recognition"

Connected through hyperedge HE-8 (induced by source paper "maintaining discrimination and fairness in class incremental learning"):
  with members:
    - "m2kd multi model and multi level knowledge distillation for incremental learning"
    - "label mapping neural networks with response consolidation for class incremental learning"
    - "three scenarios for continual learning"
    - "re evaluating continual learning scenarios a categorization and case for strong baselines"
    - "a systematic study of the class imbalance problem in convolutional neural networks"
    - "continual learning with deep generative replay"
    - "one shot face recognition by promoting underrepresented classes"
    - "continual learning through synaptic intelligence"

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)