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: "marginal replay vs conditional replay for continual learning"
  Abstract: We present a new replay-based method of continual classification learning that we term "conditional replay" which generates samples and labels together by sampling from a distribution conditioned on the class. We compare conditional replay to another replay-based continual learning paradigm (which we term "marginal replay") that generates samples independently of their class and assigns labels in a separate step. The main improvement in conditional replay is that labels for generated samples need not be inferred, which reduces the margin for error in complex continual classification learning tasks. We demonstrate the effectiveness of this approach using novel and standard benchmarks constructed from MNIST and FashionMNIST data, and compare to the regularization-based EWC method.

Connected through hyperedge HE-1 (induced by source paper "continual reinforcement learning deployed in real life using policy distillation and sim2real transfer"):
  with members:
    - "s trigger continual state representation learning via self triggered generative replay"
    - "s rl toolbox environments datasets and evaluation metrics for state representation learning"
    - "sim to real reinforcement learning for deformable object manipulation"
    - "continuous learning in single incremental task scenarios"
    - "state representation learning for control an overview"
    - "life long disentangled representation learning with cross domain latent homologies"
    - "curious intrinsically motivated modular multi goal reinforcement learning"
    - "memory replay gans learning to generate images from new categories without forgetting"

Connected through hyperedge HE-2 (induced by source paper "online learned continual compression with stacked quantization modules"):
  with members:
    - "decoupled greedy learning of cnns"
    - "generating diverse high fidelity images with vq vae 2"
    - "unsupervised state representation learning in atari"
    - "continual learning with tiny episodic memories"
    - "compressing the input for cnns with the first order scattering transform"
    - "experience replay for continual learning"
    - "efficient lifelong learning with a gem"
    - "deep generative modeling of lidar data"

Connected through hyperedge HE-3 (induced by source paper "regularization shortcomings for continual learning"):
  with members:
    - "online learned continual compression with stacked quantization modules"
    - "continual lifelong learning with neural networks a review"
    - "large scale incremental learning"
    - "continual learning for robotics definition framework learning strategies opportunities and challenges"
    - "online continual learning with maximally interfered retrieval"
    - "continual reinforcement learning deployed in real life using policy distillation and sim2real transfer"
    - "discorl continual reinforcement learning via policy distillation"
    - "deep learning for classical japanese literature"

Connected through hyperedge HE-4 (induced by source paper "online continual learning with maximally interfered retrieval"):
  with members:
    - "continual learning with tiny episodic memories"
    - "continual classification learning using generative models"
    - "task free continual learning"
    - "experience replay for continual learning"
    - "efficient lifelong learning with a gem"
    - "reinforced continual learning"
    - "riemannian walk for incremental learning understanding forgetting and intransigence"
    - "generative models from the perspective of continual learning"

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)