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: "m2kd multi model and multi level knowledge distillation for incremental learning"
  Abstract: Incremental learning targets at achieving good performance on new categories without forgetting old ones. Knowledge distillation has been shown critical in preserving the performance on old classes. Conventional methods, however, sequentially distill knowledge only from the last model, leading to performance degradation on the old classes in later incremental learning steps. In this paper, we propose a multi-model and multi-level knowledge distillation strategy. Instead of sequentially distilling knowledge only from the last model, we directly leverage all previous model snapshots. In addition, we incorporate an auxiliary distillation to further preserve knowledge encoded at the intermediate feature levels. To make the model more memory efficient, we adapt mask based pruning to reconstruct all previous models with a small memory footprint. Experiments on standard incremental learning benchmarks show that our method preserves the knowledge on old classes better and improves the overall performance over standard distillation techniques.

Connected through hyperedge HE-1 (induced by source paper "scail classifier weights scaling for class incremental learning"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "incremental learning with maximum entropy regularization rethinking forgetting and intransigence"
    - "growing a brain fine tuning by increasing model capacity"
    - "large scale incremental learning"
    - "do better imagenet models transfer better"
    - "revisiting distillation and incremental classifier learning"
    - "deesil deep shallow incremental learning"
    - "end to end incremental learning"

Connected through hyperedge HE-2 (induced by source paper "faster ilod incremental learning for object detectors based on faster rcnn"):
  with members:
    - "rilod near real time incremental learning for object detection at the edge"
    - "large scale incremental learning"
    - "soft sampling for robust object detection"
    - "end to end incremental learning"
    - "focal loss for dense object detection"
    - "incremental learning of object detectors without catastrophic forgetting"
    - "icarl incremental classifier and representation learning"
    - "learning without forgetting"

Connected through hyperedge HE-3 (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"
    - "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"
    - "tree cnn a deep convolutional neural network for lifelong learning"

Connected through hyperedge HE-4 (induced by source paper "incremental learning techniques for semantic segmentation"):
  with members:
    - "large scale 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 learning for semantic segmentation of large scale remote sensing data"
    - "incremental training of deep convolutional neural networks"
    - "game theoretic analysis of road user safety scenarios involving autonomous vehicles"

Connected through hyperedge HE-5 (induced by source paper "maintaining discrimination and fairness in class incremental learning"):
  with members:
    - "class incremental learning via deep model consolidation"
    - "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)