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: "tree cnn a deep convolutional neural network for lifelong learning"
  Abstract: In recent years, Convolutional Neural Networks (CNNs) have shown remarkable performance in many computer vision tasks such as object recognition and detection. However, complex training issues, such as "catastrophic forgetting" and hyper-parameter tuning, make incremental learning in CNNs a difficult challenge. In this paper, we propose a hierarchical deep neural network, with CNNs at multiple levels, and a corresponding training method for lifelong learning. The network grows in a tree-like manner to accommodate the new classes of data without losing the ability to identify the previously trained classes. The proposed network was tested on CIFAR-10 and CIFAR-100 datasets, and compared against the method of fine tuning specific layers of a conventional CNN. We obtained comparable accuracies and achieved 40% and 20% reduction in training effort in CIFAR-10 and CIFAR 100 respectively. The network was able to organize the incoming classes of data into feature-driven super-classes. Our model improves upon existing hierarchical CNN models by adding the capability of self-growth and also yields important observations on feature selective classification.

Connected through hyperedge HE-1 (induced by source paper "structured compression and sharing of representational space for continual learning"):
  with members:
    - "learn to grow a continual structure learning framework for overcoming catastrophic forgetting"
    - "continual lifelong learning with neural networks a review"
    - "gradient based sample selection for online continual learning"
    - "re evaluating continual learning scenarios a categorization and case for strong baselines"
    - "a low effort approach to structured cnn design using pca"
    - "alleviating catastrophic forgetting using context dependent gating and synaptic stabilization"
    - "generative replay with feedback connections as a general strategy for continual learning"
    - "packnet adding multiple tasks to a single network by iterative pruning"

Connected through hyperedge HE-2 (induced by source paper "frosting weights for better continual training"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "meta learning representations for continual learning"
    - "which tasks should be learned together in multi task learning"
    - "online continual learning with maximally interfered retrieval"
    - "continual learning with tiny episodic memories"
    - "supportnet solving catastrophic forgetting in class incremental learning with support data"
    - "model distillation with knowledge transfer from face classification to alignment and verification"
    - "tree cnn a hierarchical deep convolutional neural network for incremental learning"

Connected through hyperedge HE-3 (induced by source paper "3d point cloud generative adversarial network based on tree structured graph convolutions"):
  with members:
    - "translating and segmenting multimodal medical volumes with cycle and shape consistency generative adversarial network"
    - "multiresolution tree networks for 3d point cloud processing"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "conditional image to image translation"
    - "dynamic graph cnn for learning on point clouds"
    - "vital visual tracking via adversarial learning"
    - "generative image inpainting with contextual attention"
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"

Connected through hyperedge HE-4 (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-5 (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"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "incremental training of deep convolutional neural networks"

Connected through hyperedge HE-6 (induced by source paper "uncertainty guided continual learning with bayesian neural networks"):
  with members:
    - "large scale incremental learning"
    - "facilitating bayesian continual learning by natural gradients and stein gradients"
    - "overcoming catastrophic forgetting with unlabeled data in the wild"
    - "task free continual learning"
    - "uncertainty estimations by softplus normalization in bayesian convolutional neural networks with variational inference"
    - "efficient lifelong learning with a gem"
    - "overcoming catastrophic forgetting with hard attention to the task"
    - "end to end incremental learning"

Connected through hyperedge HE-7 (induced by source paper "interpretable image recognition with hierarchical prototypes"):
  with members:
    - "this looks like that deep learning for interpretable image recognition"
    - "branchconnect image categorization with learned branch connections"
    - "does your model know the digit 6 is not a cat a less biased evaluation of outlier detectors"
    - "why relu networks yield high confidence predictions far away from the training data and how to mitigate the problem"
    - "deep learning for case based reasoning through prototypes a neural network that explains its predictions"
    - "network dissection quantifying interpretability of deep visual representations"
    - "axiomatic attribution for deep networks"
    - "b cnn branch convolutional neural network for hierarchical classification"

Connected through hyperedge HE-8 (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"
    - "incremental learning for semantic segmentation of large scale remote sensing data"
    - "incremental training of deep convolutional neural networks"
    - "game theoretic analysis of road user safety scenarios involving autonomous vehicles"

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)