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: "less forgetful learning for domain expansion in deep neural networks"
  Abstract: Expanding the domain that deep neural network has already learned without accessing old domain data is a challenging task because deep neural networks forget previously learned information when learning new data from a new domain. In this paper, we propose a less-forgetful learning method for the domain expansion scenario. While existing domain adaptation techniques solely focused on adapting to new domains, the proposed technique focuses on working well with both old and new domains without needing to know whether the input is from the old or new domain. First, we present two naive approaches which will be problematic, then we provide a new method using two proposed properties for less-forgetful learning. Finally, we prove the effectiveness of our method through experiments on image classification tasks. All datasets used in the paper, will be released on our website for someone's follow-up study.

Connected through hyperedge HE-1 (induced by source paper "overcoming catastrophic forgetting with unlabeled data in the wild"):
  with members:
    - "supportnet solving catastrophic forgetting in class incremental learning with support data"
    - "incremental classifier learning with generative adversarial networks"
    - "revisiting distillation and incremental classifier learning"
    - "spectral normalization for generative adversarial networks"
    - "hierarchical novelty detection for visual object recognition"
    - "piggyback adapting a single network to multiple tasks by learning to mask weights"
    - "exploring the limits of weakly supervised pretraining"
    - "keep and learn continual learning by constraining the latent space for knowledge preservation in neural networks"

Connected through hyperedge HE-2 (induced by source paper "efficient continual learning in neural networks with embedding regularization"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "don t forget there is more than forgetting new metrics for continual learning"
    - "rotate your networks better weight consolidation and less catastrophic forgetting"
    - "efficient lifelong learning with a gem"
    - "pseudo recursal solving the catastrophic forgetting problem in deep neural networks"
    - "progressive neural networks"
    - "neural graph machines learning neural networks using graphs"
    - "deep image prior"

Connected through hyperedge HE-3 (induced by source paper "continual learning by asymmetric loss approximation with single side overestimation"):
  with members:
    - "riemannian walk for incremental learning understanding forgetting and intransigence"
    - "overcoming catastrophic forgetting with hard attention to the task"
    - "packnet adding multiple tasks to a single network by iterative pruning"
    - "memory aware synapses learning what not to forget"
    - "progressive neural networks"
    - "lifelong learning with dynamically expandable networks"
    - "continual learning with deep generative replay"
    - "continual learning through synaptic intelligence"

Connected through hyperedge HE-4 (induced by source paper "regularize expand and compress multi task based lifelong learning via nonexpansive automl"):
  with members:
    - "rotate your networks better weight consolidation and less catastrophic forgetting"
    - "efficient neural architecture search via parameter sharing"
    - "progressive neural networks"
    - "lifelong learning with dynamically expandable networks"
    - "efficient architecture search by network transformation"
    - "continual learning through synaptic intelligence"
    - "gradient episodic memory for continual learning"
    - "pathnet evolution channels gradient descent in super neural networks"

Connected through hyperedge HE-5 (induced by source paper "hc net memory based incremental dual network system for continual learning"):
  with members:
    - "packnet adding multiple tasks to a single network by iterative pruning"
    - "progressive neural networks"
    - "continual learning with deep generative replay"
    - "fearnet brain inspired model for incremental learning"
    - "ssd single shot multibox detector"
    - "learning without forgetting"
    - "pruning filters for efficient convnets"
    - "r fcn object detection via region based fully convolutional networks"

Connected through hyperedge HE-6 (induced by source paper "stacknet stacking parameters for continual learning"):
  with members:
    - "packnet adding multiple tasks to a single network by iterative pruning"
    - "lifelong learning with dynamically expandable networks"
    - "routing networks adaptive selection of non linear functions for multi task learning"
    - "continual learning with deep generative replay"
    - "encoder based lifelong learning"
    - "overcoming catastrophic forgetting in neural networks"
    - "learning without forgetting"
    - "pruning filters for efficient convnets"

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)