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: "active long term memory networks"
  Abstract: Continual Learning in artificial neural networks suffers from interference and forgetting when different tasks are learned sequentially. This paper introduces the Active Long Term Memory Networks (A-LTM), a model of sequential multi-task deep learning that is able to maintain previously learned association between sensory input and behavioral output while acquiring knew knowledge. A-LTM exploits the non-convex nature of deep neural networks and actively maintains knowledge of previously learned, inactive tasks using a distillation loss. Distortions of the learned input-output map are penalized but hidden layers are free to transverse towards new local optima that are more favorable for the multi-task objective. We re-frame the McClelland's seminal Hippocampal theory with respect to Catastrophic Inference (CI) behavior exhibited by modern deep architectures trained with back-propagation and inhomogeneous sampling of latent factors across epochs. We present empirical results of non-trivial CI during continual learning in Deep Linear Networks trained on the same task, in Convolutional Neural Networks when the task shifts from predicting semantic to graphical factors and during domain adaptation from simple to complex environments. We present results of the A-LTM model's ability to maintain viewpoint recognition learned in the highly controlled iLab-20M dataset with 10 object categories and 88 camera viewpoints, while adapting to the unstructured domain of Imagenet with 1,000 object categories.

Connected through hyperedge HE-1 (induced by source paper "growing a brain fine tuning by increasing model capacity"):
  with members:
    - "knowledge transfer in deep block modular neural networks"
    - "progressive neural networks"
    - "a deep hierarchical approach to lifelong learning in minecraft"
    - "a growing long term episodic semantic memory"
    - "one shot learning with memory augmented neural networks"
    - "learning feed forward one shot learners"
    - "cross stitch networks for multi task learning"
    - "what makes imagenet good for transfer learning"

Connected through hyperedge HE-2 (induced by source paper "knowledge distillation for incremental learning in semantic segmentation"):
  with members:
    - "incremental learning techniques for semantic segmentation"
    - "recent progress in semantic image segmentation"
    - "large scale incremental learning"
    - "m2kd multi model and multi level knowledge distillation for incremental learning"
    - "adversarial learning and self teaching techniques for domain adaptation in semantic segmentation"
    - "similarity preserving knowledge distillation"
    - "learning without memorizing"
    - "incremental classifier learning with generative adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "incremental learning techniques for semantic segmentation"):
  with members:
    - "large scale incremental learning"
    - "m2kd multi model and multi level knowledge distillation for incremental learning"
    - "tree cnn a hierarchical deep convolutional neural network for incremental learning"
    - "learning without memorizing"
    - "incremental classifier learning with generative adversarial networks"
    - "tree cnn a deep convolutional neural network for lifelong learning"
    - "incremental learning for semantic segmentation of large scale remote sensing data"
    - "incremental training of deep convolutional neural networks"

Connected through hyperedge HE-4 (induced by source paper "policy consolidation for continual reinforcement learning"):
  with members:
    - "experience replay for continual learning"
    - "reinforced continual learning"
    - "selective experience replay for lifelong learning"
    - "progressive neural networks"
    - "continual learning with deep generative replay"
    - "emergent complexity via multi agent competition"
    - "continual learning through synaptic intelligence"
    - "continuous adaptation via meta learning in nonstationary and competitive environments"

Connected through hyperedge HE-5 (induced by source paper "knowledge flow improve upon your teachers"):
  with members:
    - "growing a brain fine tuning by increasing model capacity"
    - "progressive neural networks"
    - "analysis and optimization of convolutional neural network architectures"
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"
    - "emnist an extension of mnist to handwritten letters"
    - "pathnet evolution channels gradient descent in super neural networks"
    - "distral robust multitask reinforcement learning"
    - "proximal policy optimization algorithms"

Connected through hyperedge HE-6 (induced by source paper "closed loop memory gan for continual learning"):
  with members:
    - "memory replay gans learning to generate images from new categories without forgetting"
    - "continual learning with deep generative replay"
    - "emnist an extension of mnist to handwritten letters"
    - "gradient episodic memory for continual learning"
    - "pathnet evolution channels gradient descent in super neural networks"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "fearnet brain inspired model for incremental learning"
    - "icarl incremental classifier and representation learning"

Connected through hyperedge HE-7 (induced by source paper "incremental learning for semantic segmentation of large scale remote sensing data"):
  with members:
    - "supportnet solving catastrophic forgetting in class incremental learning with support data"
    - "incremental classifier learning with generative adversarial networks"
    - "rotate your networks better weight consolidation and less catastrophic forgetting"
    - "reinforced continual learning"
    - "ternausnetv2 fully convolutional network for instance segmentation"
    - "ternausnet u net with vgg11 encoder pre trained on imagenet for image segmentation"
    - "end to end incremental learning"
    - "packnet adding multiple tasks to a single network by iterative pruning"

Connected through hyperedge HE-8 (induced by source paper "life long disentangled representation learning with cross domain latent homologies"):
  with members:
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "continual learning in generative adversarial nets"
    - "progressive neural networks"
    - "variational inference of disentangled latent concepts from unlabeled observations"
    - "continual learning with deep generative replay"
    - "continual learning through synaptic intelligence"
    - "opening the black box of deep neural networks via information"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"

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)