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: "large scale classification of fine art paintings learning the right metric on the right feature"
  Abstract: In the past few years, the number of fine-art collections that are digitized and publicly available has been growing rapidly. With the availability of such large collections of digitized artworks comes the need to develop multimedia systems to archive and retrieve this pool of data. Measuring the visual similarity between artistic items is an essential step for such multimedia systems, which can benefit more high-level multimedia tasks. In order to model this similarity between paintings, we should extract the appropriate visual features for paintings and find out the best approach to learn the similarity metric based on these features. We investigate a comprehensive list of visual features and metric learning approaches to learn an optimized similarity measure between paintings. We develop a machine that is able to make aesthetic-related semantic-level judgments, such as predicting a painting's style, genre, and artist, as well as providing similarity measures optimized based on the knowledge available in the domain of art historical interpretation. Our experiments show the value of using this similarity measure for the aforementioned prediction tasks.

Connected through hyperedge HE-1 (induced by source paper "nettailor tuning the architecture not just the weights"):
  with members:
    - "piggyback adapting a single network to multiple tasks by learning to mask weights"
    - "darts differentiable architecture search"
    - "packnet adding multiple tasks to a single network by iterative pruning"
    - "progressive neural architecture search"
    - "learning transferable architectures for scalable image recognition"
    - "progressive neural networks"
    - "lifelong learning with dynamically expandable networks"
    - "teacher student curriculum learning"

Connected through hyperedge HE-2 (induced by source paper "compacting picking and growing for unforgetting continual learning"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "a comprehensive application oriented study of catastrophic forgetting in dnns"
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "learning to remember a synaptic plasticity driven framework for continual learning"
    - "learning without memorizing"
    - "task free continual learning"
    - "incremental classifier learning with generative adversarial networks"
    - "houdini lifelong learning as program synthesis"

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"
    - "adding new tasks to a single network with weight transformations using binary masks"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "context aware embeddings for automatic art analysis"):
  with members:
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "a style aware content loss for real time hd style transfer"
    - "how to read paintings semantic art understanding with multi modal retrieval"
    - "graph embedding techniques applications and performance a survey"
    - "multi task learning as multi objective optimization"
    - "an overview of multi task learning in deep neural networks"
    - "deep multi task representation learning a tensor factorisation approach"
    - "node2vec scalable feature learning for networks"

Connected through hyperedge HE-5 (induced by source paper "historical and modern features for buddha statue classification"):
  with members:
    - "context aware embeddings for automatic art analysis"
    - "how to read paintings semantic art understanding with multi modal retrieval"
    - "joint 3d face reconstruction and dense alignment with position map regression network"
    - "the shape of art history in the eyes of the machine"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "omniart multi task deep learning for artistic data analysis"
    - "node2vec scalable feature learning for networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-6 (induced by source paper "linking art through human poses"):
  with members:
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "how to read paintings semantic art understanding with multi modal retrieval"
    - "weakly supervised object detection in artworks"
    - "fine tuning cnn image retrieval with no human annotation"
    - "deep shape matching"
    - "rmpe regional multi person pose estimation"
    - "detecting people in artwork with cnns"
    - "you only look once unified real time object detection"

Connected through hyperedge HE-7 (induced by source paper "information foraging for enhancing implicit feedback in content based image recommendation"):
  with members:
    - "effects of foraging in personalized content based image recommendation"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-8 (induced by source paper "salient slices improved neural network training and performance with image entropy"):
  with members:
    - "recognizing image style"

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)