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: "a strategy for an uncompromising incremental learner"
  Abstract: Multi-class supervised learning systems require the knowledge of the entire range of labels they predict. Often when learnt incrementally, they suffer from catastrophic forgetting. To avoid this, generous leeways have to be made to the philosophy of incremental learning that either forces a part of the machine to not learn, or to retrain the machine again with a selection of the historic data. While these hacks work to various degrees, they do not adhere to the spirit of incremental learning. In this article, we redefine incremental learning with stringent conditions that do not allow for any undesirable relaxations and assumptions. We design a strategy involving generative models and the distillation of dark knowledge as a means of hallucinating data along with appropriate targets from past distributions. We call this technique, phantom sampling.We show that phantom sampling helps avoid catastrophic forgetting during incremental learning. Using an implementation based on deep neural networks, we demonstrate that phantom sampling dramatically avoids catastrophic forgetting. We apply these strategies to competitive multi-class incremental learning of deep neural networks. Using various benchmark datasets and through our strategy, we demonstrate that strict incremental learning could be achieved. We further put our strategy to test on challenging cases, including cross-domain increments and incrementing on a novel label space. We also propose a trivial extension to unbounded-continual learning and identify potential for future development.

Connected through hyperedge HE-1 (induced by source paper "openloris object a robotic vision dataset and benchmark for lifelong deep learning"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "generative adversarial networks a survey and taxonomy"
    - "neural dynamics discovery via gaussian process recurrent neural networks"
    - "are we ready for service robots the openloris scene datasets for lifelong slam"
    - "neuroscore a brain inspired evaluation metric for generative adversarial networks"
    - "incremental classifier learning with generative adversarial networks"
    - "the limits and potentials of deep learning for robotics"
    - "don t forget there is more than forgetting new metrics for continual learning"

Connected through hyperedge HE-2 (induced by source paper "mnemonics training multi class incremental learning without forgetting"):
  with members:
    - "large scale incremental learning"
    - "self tuning networks bilevel optimization of hyperparameters using structured best response functions"
    - "pytorch an imperative style high performance deep learning library"
    - "canet class agnostic segmentation networks with iterative refinement and attentive few shot learning"
    - "meta transfer learning for few shot learning"
    - "learning to learn without forgetting by maximizing transfer and minimizing interference"
    - "deep generative dual memory network for continual learning"
    - "end to end incremental learning"

Connected through hyperedge HE-3 (induced by source paper "learning to remember from a multi task teacher"):
  with members:
    - "backpropamine training self modifying neural networks with differentiable neuromodulated plasticity"
    - "overcoming multi model forgetting"
    - "meta learning representations for continual learning"
    - "understanding and correcting pathologies in the training of learned optimizers"
    - "differentiable plasticity training plastic neural networks with backpropagation"
    - "reviving and improving recurrent back propagation"
    - "generative replay with feedback connections as a general strategy for continual learning"
    - "overcoming catastrophic forgetting with hard attention to the task"

Connected through hyperedge HE-4 (induced by source paper "large scale incremental learning"):
  with members:
    - "end to end incremental learning"
    - "progressive neural networks"
    - "continual learning with deep generative replay"
    - "one shot face recognition by promoting underrepresented classes"
    - "encoder based lifelong learning"
    - "incremental learning of object detectors without catastrophic forgetting"
    - "gradient episodic memory for continual learning"
    - "icarl incremental classifier and representation learning"

Connected through hyperedge HE-5 (induced by source paper "three scenarios for continual learning"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "overcoming catastrophic forgetting with unlabeled data in the wild"
    - "incremental classifier learning with generative adversarial networks"
    - "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"
    - "overcoming catastrophic forgetting with hard attention to the task"

Connected through hyperedge HE-6 (induced by source paper "incremental classifier learning based on pedcc loss and cosine distance"):
  with members:
    - "a new loss function for cnn classifier based on pre defined evenly distributed class centroids"
    - "incremental classifier learning with generative adversarial networks"
    - "progressive neural networks"
    - "wide residual networks"
    - "conditional generative adversarial nets"

Connected through hyperedge HE-7 (induced by source paper "generative replay with feedback connections as a general strategy for continual learning"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "three scenarios for continual learning"
    - "incremental classifier learning with generative adversarial networks"
    - "alleviating catastrophic forgetting using context dependent gating and synaptic stabilization"
    - "deep generative dual memory network for continual learning"
    - "pseudo recursal solving the catastrophic forgetting problem in deep neural networks"
    - "overcoming catastrophic forgetting with hard attention to the task"
    - "scalable recollections for continual lifelong learning"

Connected through hyperedge HE-8 (induced by source paper "generative models from the perspective of continual learning"):
  with members:
    - "training discriminative models to evaluate generative ones"
    - "incremental classifier learning with generative adversarial networks"
    - "distillation techniques for pseudo rehearsal based incremental learning"
    - "pros and cons of gan evaluation measures"
    - "life long disentangled representation learning with cross domain latent homologies"
    - "memory replay gans learning to generate images from new categories without forgetting"
    - "continual learning in generative adversarial nets"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"

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)