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: "adding new tasks to a single network with weight transformations using binary masks"
  Abstract: Visual recognition algorithms are required today to exhibit adaptive abilities. Given a deep model trained on a specific, given task, it would be highly desirable to be able to adapt incrementally to new tasks, preserving scalability as the number of new tasks increases, while at the same time avoiding catastrophic forgetting issues. Recent work has shown that masking the internal weights of a given original conv-net through learned binary variables is a promising strategy. We build upon this intuition and take into account more elaborated affine transformations of the convolutional weights that include learned binary masks. We show that with our generalization it is possible to achieve significantly higher levels of adaptation to new tasks, enabling the approach to compete with fine tuning strategies by requiring slightly more than 1 bit per network parameter per additional task. Experiments on two popular benchmarks showcase the power of our approach, that achieves the new state of the art on the Visual Decathlon Challenge.

Connected through hyperedge HE-1 (induced by source paper "incremental multi domain learning with network latent tensor factorization"):
  with members:
    - "can we gain more from orthogonality regularizations in training deep cnns"
    - "piggyback adapting a single network to multiple tasks by learning to mask weights"
    - "cp decomposition with tensor power method for convolutional neural networks compression"
    - "universal representations the missing link between faces text planktons and cat breeds"
    - "learning to generalize meta learning for domain generalization"
    - "incremental learning through deep adaptation"
    - "adversarial discriminative domain adaptation"
    - "learning multiple visual domains with residual adapters"

Connected through hyperedge HE-2 (induced by source paper "the rgb d triathlon towards agile visual toolboxes for robots"):
  with members:
    - "the limits and potentials of deep learning for robotics"
    - "taskonomy disentangling task transfer learning"
    - "rgb d object detection and semantic segmentation for autonomous manipulation in clutter"
    - "piggyback adapting a single network to multiple tasks by learning to mask weights"
    - "depth cnns for rgb d scene recognition learning from scratch better than transferring from rgb cnns"
    - "3d pose regression using convolutional neural networks"
    - "packnet adding multiple tasks to a single network by iterative pruning"
    - "analyzing modular cnn architectures for joint depth prediction and semantic segmentation"

Connected through hyperedge HE-3 (induced by source paper "budget aware adapters for multi domain learning"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "slimmable neural networks"
    - "spottune transfer learning through adaptive fine tuning"
    - "piggyback adapting a single network to multiple tasks by learning to mask weights"
    - "skipnet learning dynamic routing in convolutional networks"
    - "packnet adding multiple tasks to a single network by iterative pruning"
    - "memory aware synapses learning what not to forget"
    - "progressive neural networks"

Connected through hyperedge HE-4 (induced by source paper "learning to remember a synaptic plasticity driven framework for continual learning"):
  with members:
    - "incremental classifier learning with generative adversarial networks"
    - "piggyback adapting a single network to multiple tasks by learning to mask weights"
    - "memory replay gans learning to generate images from new categories without forgetting"
    - "deep generative dual memory network for continual learning"
    - "riemannian walk for incremental learning understanding forgetting and intransigence"
    - "overcoming catastrophic forgetting with hard attention to the task"
    - "continual learning in generative adversarial nets"
    - "memory aware synapses learning what not to forget"

Connected through hyperedge HE-5 (induced by source paper "learn to grow a continual structure learning framework for overcoming catastrophic forgetting"):
  with members:
    - "supportnet solving catastrophic forgetting in class incremental learning with support data"
    - "piggyback adapting a single network to multiple tasks by learning to mask weights"
    - "darts differentiable architecture search"
    - "overcoming catastrophic forgetting with hard attention to the task"
    - "accelerating neural architecture search using performance prediction"
    - "progressive neural networks"
    - "lifelong learning with dynamically expandable networks"
    - "continual learning with deep generative replay"

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.CV(Computer Vision and Pattern Recognition)