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: "incremental learning in deep convolutional neural networks using partial network sharing"
  Abstract: Deep convolutional neural network (DCNN) based supervised learning is a widely practiced approach for large-scale image classification. However, retraining these large networks to accommodate new, previously unseen data demands high computational time and energy requirements. Also, previously seen training samples may not be available at the time of retraining. We propose an efficient training methodology and incrementally growing DCNN to learn new tasks while sharing part of the base network. Our proposed methodology is inspired by transfer learning techniques, although it does not forget previously learned tasks. An updated network for learning new set of classes is formed using previously learned convolutional layers (shared from initial part of base network) with addition of few newly added convolutional kernels included in the later layers of the network. We employed a `clone-and-branch' technique which allows the network to learn new tasks one after another without any performance loss in old tasks. We evaluated the proposed scheme on several recognition applications. The classification accuracy achieved by our approach is comparable to the regular incremental learning approach (where networks are updated with new training samples only, without any network sharing), while achieving energy efficiency, reduction in storage requirements, memory access and training time.

Connected through hyperedge HE-1 (induced by source paper "energy efficient and robust cumulative training with net2net transformation"):
  with members:
    - "tree cnn a hierarchical deep convolutional neural network for incremental learning"
    - "learning without forgetting"
    - "a baseline for detecting misclassified and out of distribution examples in neural networks"
    - "falcon feature driven selective classification for energy efficient image recognition"
    - "learning both weights and connections for efficient neural networks"
    - "deep residual learning for image recognition"
    - "net2net accelerating learning via knowledge transfer"
    - "multi task sequence to sequence learning"

Connected through hyperedge HE-2 (induced by source paper "towards federated learning robustness analytics to data heterogeneity"):
  with members:
    - "gradient boosted feature selection"
    - "decentralized online learning take benefits from others data without sharing your own to track global trend"
    - "active learning solution on distributed edge computing"
    - "federated learning strategies for improving communication efficiency"
    - "federated learning with non iid data"
    - "federated optimization distributed machine learning for on device intelligence"
    - "understanding neural networks through deep visualization"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-3 (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-4 (induced by source paper "incremental learning using a grow and prune paradigm with efficient neural networks"):
  with members:
    - "hardware guided symbiotic training for compact accurate yet execution efficient lstm"
    - "knowledge transfer in deep block modular neural networks"
    - "scann synthesis of compact and accurate neural networks"
    - "tree cnn a hierarchical deep convolutional neural network for incremental learning"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "neural voice cloning with a few samples"
    - "tree cnn a deep convolutional neural network for lifelong learning"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"

Connected through hyperedge HE-5 (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-6 (induced by source paper "modeling neural architecture search methods for deep networks"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "nettailor tuning the architecture not just the weights"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "segmentation of bleeding regions in wireless capsule endoscopy for detection of informative frames"
    - "mnasnet platform aware neural architecture search for mobile"
    - "learning transferable architectures for scalable image recognition"
    - "learning time efficient deep architectures with budgeted super networks"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"

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 "tree cnn a deep convolutional neural network for lifelong learning"):
  with members:
    - "progressive neural networks"
    - "deep convolutional neural networks as generic feature extractors"
    - "incremental learning of object detectors without catastrophic forgetting"
    - "learning without forgetting"
    - "expert gate lifelong learning with a network of experts"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "fast r cnn"
    - "fast and accurate deep network learning by exponential linear units elus"

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)