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: "oracle order robust adaptive continual learning"
  Abstract: The order of the tasks a continual learning model encounters may have large impact on the performance of each task, as well as the task-average performance. This order-sensitivity may cause serious problems in real-world scenarios where fairness plays a critical role (e.g. medical diagnosis). To tackle this problem, we propose a novel order-robust continual learning method, which instead of learning a completely shared set of weights, represent the parameters for each task as a sum of task-shared parameters that captures generic representations and task-adaptive parameters capturing task-specific ones, where the latter is factorized into sparse low-rank matrices in order to minimize capacity increase. With such parameter decomposition, when training for a new task, the task-adaptive parameters for earlier tasks remain mostly unaffected, where we update them only to reflect the changes made to the task-shared parameters. This prevents catastrophic forgetting for old tasks and at the same time make the model less sensitive to the task arrival order. We validate our Order-Robust Adaptive Continual LEarning (ORACLE) method on multiple benchmark datasets against state-of-the-art continual learning methods, and the results show that it largely outperforms those strong baselines with significantly less increase in capacity and training time, as well as obtains smaller performance disparity for each task with different order sequences.

Connected through hyperedge HE-1 (induced by source paper "variational federated multi task learning"):
  with members:
    - "federated collaborative filtering for privacy preserving personalized recommendation system"
    - "knowledge transfer in deep block modular neural networks"
    - "applied federated learning improving google keyboard query suggestions"
    - "flipout efficient pseudo independent weight perturbations on mini batches"
    - "federated optimization for heterogeneous networks"
    - "leaf a benchmark for federated settings"
    - "communication efficient on device machine learning federated distillation and augmentation under non iid private data"
    - "memory replay gans learning to generate images from new categories without forgetting"

Connected through hyperedge HE-2 (induced by source paper "progressive memory banks for incremental domain adaptation"):
  with members:
    - "episodic memory in lifelong language learning"
    - "incremental learning with maximum entropy regularization rethinking forgetting and intransigence"
    - "overcoming catastrophic forgetting problem by weight consolidation and long term memory"
    - "memory augmented dialogue management for task oriented dialogue systems"
    - "efficient lifelong learning with a gem"
    - "mem2seq effectively incorporating knowledge bases into end to end task oriented dialog systems"
    - "reinforced continual learning"
    - "memory replay gans learning to generate images from new categories without forgetting"

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)