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: "scalable recollections for continual lifelong learning"
  Abstract: Given the recent success of Deep Learning applied to a variety of single tasks, it is natural to consider more human-realistic settings. Perhaps the most difficult of these settings is that of continual lifelong learning, where the model must learn online over a continuous stream of non-stationary data. A successful continual lifelong learning system must have three key capabilities: it must learn and adapt over time, it must not forget what it has learned, and it must be efficient in both training time and memory. Recent techniques have focused their efforts primarily on the first two capabilities while questions of efficiency remain largely unexplored. In this paper, we consider the problem of efficient and effective storage of experiences over very large time-frames. In particular we consider the case where typical experiences are O(n) bits and memories are limited to O(k) bits for k << n. We present a novel scalable architecture and training algorithm in this challenging domain and provide an extensive evaluation of its performance. Our results show that we can achieve considerable gains on top of state-of-the-art methods such as GEM.

Connected through hyperedge HE-1 (induced by source paper "on the role of weight sharing during deep option learning"):
  with members:
    - "routing networks and the challenges of modular and compositional computation"
    - "reinforcement learning without backpropagation or a clock"
    - "learning abstract options"
    - "how many random seeds statistical power analysis in deep reinforcement learning experiments"
    - "learning to learn without forgetting by maximizing transfer and minimizing interference"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "progressive neural networks"
    - "routing networks adaptive selection of non linear functions for multi task learning"

Connected through hyperedge HE-2 (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"
    - "marginal replay vs conditional replay for continual learning"
    - "riemannian walk for incremental learning understanding forgetting and intransigence"

Connected through hyperedge HE-3 (induced by source paper "forget me not reducing catastrophic forgetting for domain adaptation in reading comprehension"):
  with members:
    - "the natural language decathlon multitask learning as question answering"
    - "know what you don t know unanswerable questions for squad"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "learning to learn without forgetting by maximizing transfer and minimizing interference"
    - "overcoming catastrophic forgetting with hard attention to the task"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"
    - "attention is all you need"

Connected through hyperedge HE-4 (induced by source paper "learning to learn without forgetting by maximizing transfer and minimizing interference"):
  with members:
    - "on first order meta learning algorithms"
    - "learning abstract options"
    - "reptile a scalable metalearning algorithm"
    - "riemannian walk for incremental learning understanding forgetting and intransigence"
    - "overcoming catastrophic forgetting with hard attention to the task"
    - "learning robust options"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "lifelong learning with dynamically expandable networks"

Connected through hyperedge HE-5 (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"
    - "a strategy for an uncompromising incremental learner"

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)