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: "on training recurrent neural networks for lifelong learning"
  Abstract: Catastrophic forgetting and capacity saturation are the central challenges of any parametric lifelong learning system. In this work, we study these challenges in the context of sequential supervised learning with emphasis on recurrent neural networks. To evaluate the models in the lifelong learning setting, we propose a curriculum-based, simple, and intuitive benchmark where the models are trained on tasks with increasing levels of difficulty. To measure the impact of catastrophic forgetting, the model is tested on all the previous tasks as it completes any task. As a step towards developing true lifelong learning systems, we unify Gradient Episodic Memory (a catastrophic forgetting alleviation approach) and Net2Net(a capacity expansion approach). Both these models are proposed in the context of feedforward networks and we evaluate the feasibility of using them for recurrent networks. Evaluation on the proposed benchmark shows that the unified model is more suitable than the constituent models for lifelong learning setting.

Connected through hyperedge HE-1 (induced by source paper "evaluating logical generalization in graph neural networks"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "enhancing the transformer with explicit relational encoding for math problem solving"
    - "are powerful graph neural nets necessary a dissection on graph classification"
    - "clutrr a diagnostic benchmark for inductive reasoning from text"
    - "continual learning a comparative study on how to defy forgetting in classification tasks"
    - "graph neural tangent kernel fusing graph neural networks with graph kernels"
    - "pytorch an imperative style high performance deep learning library"
    - "weisfeiler and leman go neural higher order graph neural networks"

Connected through hyperedge HE-2 (induced by source paper "frosting weights for better continual training"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "meta learning representations for continual learning"
    - "which tasks should be learned together in multi task learning"
    - "online continual learning with maximally interfered retrieval"
    - "continual learning with tiny episodic memories"
    - "supportnet solving catastrophic forgetting in class incremental learning with support data"
    - "model distillation with knowledge transfer from face classification to alignment and verification"
    - "tree cnn a hierarchical deep convolutional neural network for incremental learning"

Connected through hyperedge HE-3 (induced by source paper "lifelong sequential modeling with personalized memorization for user response prediction"):
  with members:
    - "collaborative memory network for recommendation systems"
    - "learning multi touch conversion attribution with dual attention mechanisms for online advertising"
    - "next item recommendation with self attention"
    - "bidding machine learning to bid for directly optimizing profits in display advertising"
    - "recurrent neural networks for long and short term sequential recommendation"
    - "recurrent neural networks with top k gains for session based recommendations"
    - "personalized top n sequential recommendation via convolutional sequence embedding"
    - "self attentive sequential recommendation"

Connected through hyperedge HE-4 (induced by source paper "clutrr a diagnostic benchmark for inductive reasoning from text"):
  with members:
    - "annotation artifacts in natural language inference data"
    - "systematic generalization what is required and can it be learned"
    - "relational recurrent neural networks"
    - "deep contextualized word representations"
    - "embedding logical queries on knowledge graphs"
    - "compositional attention networks for machine reasoning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "how much reading does reading comprehension require a critical investigation of popular benchmarks"

Connected through hyperedge HE-5 (induced by source paper "lamol language modeling for lifelong language learning"):
  with members:
    - "episodic memory in lifelong language learning"
    - "continual learning for sentence representations using conceptors"
    - "lifelong domain word embedding via meta learning"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "lifelong learning for sentiment classification"
    - "the natural language decathlon multitask learning as question answering"
    - "efficient lifelong learning with a gem"
    - "exploring the challenges towards lifelong fact learning"

Connected through hyperedge HE-6 (induced by source paper "lamal language modeling is all you need for lifelong language learning"):
  with members:
    - "episodic memory in lifelong language learning"
    - "continual learning for sentence representations using conceptors"
    - "lifelong domain word embedding via meta learning"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "lifelong learning for sentiment classification"
    - "the natural language decathlon multitask learning as question answering"
    - "efficient lifelong learning with a gem"
    - "exploring the challenges towards lifelong fact learning"

Connected through hyperedge HE-7 (induced by source paper "neural architecture search for class incremental learning"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "efficient lifelong learning with a gem"
    - "reinforced continual learning"
    - "generative replay with feedback connections as a general strategy for continual learning"
    - "understanding the impact of entropy on policy optimization"
    - "lifelong learning with dynamically expandable networks"
    - "efficient architecture search by network transformation"
    - "gradient episodic memory for 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)