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: "how to read paintings semantic art understanding with multi modal retrieval"
  Abstract: Automatic art analysis has been mostly focused on classifying artworks into different artistic styles. However, understanding an artistic representation involves more complex processes, such as identifying the elements in the scene or recognizing author influences. We present SemArt, a multi-modal dataset for semantic art understanding. SemArt is a collection of fine-art painting images in which each image is associated to a number of attributes and a textual artistic comment, such as those that appear in art catalogues or museum collections. To evaluate semantic art understanding, we envisage the Text2Art challenge, a multi-modal retrieval task where relevant paintings are retrieved according to an artistic text, and vice versa. We also propose several models for encoding visual and textual artistic representations into a common semantic space. Our best approach is able to find the correct image within the top 10 ranked images in the 45.5% of the test samples. Moreover, our models show remarkable levels of art understanding when compared against human evaluation.

Connected through hyperedge HE-1 (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"
    - "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"
    - "the more you know using knowledge graphs for image classification"

Connected through hyperedge HE-2 (induced by source paper "historical and modern features for buddha statue classification"):
  with members:
    - "context aware embeddings for automatic art analysis"
    - "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"
    - "large scale classification of fine art paintings learning the right metric on the right feature"

Connected through hyperedge HE-3 (induced by source paper "linking art through human poses"):
  with members:
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "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"
    - "large scale classification of fine art paintings learning the right metric on the right feature"

Connected through hyperedge HE-4 (induced by source paper "understanding art through multi modal retrieval in paintings"):
  with members:
    - "context aware embeddings for automatic art analysis"
    - "a style aware content loss for real time hd style transfer"
    - "starspace embed all the things"
    - "node2vec scalable feature learning for networks"
    - "vqa visual question answering"
    - "deep residual learning for image recognition"
    - "microsoft coco common objects in context"
    - "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)