human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "continual learning in neural networks"):
  Abstract: Artificial neural networks have exceeded human-level performance in accomplishing several individual tasks (e.g. voice recognition, object recognition, and video games). However, such success remains modest compared to human intelligence that can learn and perform an unlimited number of tasks. Humans' ability of learning and accumulating knowledge over their lifetime is an essential aspect of their intelligence. Continual machine learning aims at a higher level of machine intelligence through providing the artificial agents with the ability to learn online from a non-stationary and never-ending stream of data. A key component of such a never-ending learning process is to overcome the catastrophic forgetting of previously seen data, a problem that neural networks are well known to suffer from. The work described in this thesis has been dedicated to the investigation of continual learning and solutions to mitigate the forgetting phenomena in neural networks. To approach the continual learning problem, we first assume a task incremental setting where tasks are received one at a time and data from previous tasks are not stored. Since the task incremental setting can't be assumed in all continual learning scenarios, we also study the more general online continual setting. We consider an infinite stream of data drawn from a non-stationary distribution with a supervisory or self-supervisory training signal. The proposed methods in this thesis have tackled important aspects of continual learning. They were evaluated on different benchmarks and over various learning sequences. Advances in the state of the art of continual learning have been shown and challenges for bringing continual learning into application were critically identified.

Includes members:
  Member "class incremental learning via deep model consolidation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "ternary feature masks continual learning without any forgetting")
  Member "knowledge transfer in deep block modular neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "brain inspired model for incremental learning using a few examples")
  Member "meta learning representations for continual learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "online fast adaptation and knowledge accumulation a new approach to continual learning")
  Member "continual reinforcement learning in 3d non stationary environments":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "online fast adaptation and knowledge accumulation a new approach to continual learning")
  Member "continual learning with tiny episodic memories":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "frosting weights for better continual training")
  Member "continual classification learning using generative models":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "continual learning a comparative study on how to defy forgetting in classification tasks")
  Member "task free continual learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "on catastrophic interference in atari 2600 games")
  Member "re evaluating continual learning scenarios a categorization and case for strong baselines":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a neural dirichlet process mixture model for task free continual learning")

Question: Please tell me which class the source paper 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)