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: "human curation and convnets powering item to item recommendations on pinterest"
  Abstract: This paper presents Pinterest Related Pins, an item-to-item recommendation system that combines collaborative filtering with content-based ranking. We demonstrate that signals derived from user curation, the activity of users organizing content, are highly effective when used in conjunction with content-based ranking. This paper also demonstrates the effectiveness of visual features, such as image or object representations learned from convnets, in improving the user engagement rate of our item-to-item recommendation system.

Connected through hyperedge HE-1 (induced by source paper "related pins at pinterest the evolution of a real world recommender system"):
  with members:
    - "visual discovery at pinterest"
    - "pixie a system for recommending 3 billion items to 200 million users in real time"
    - "wide deep learning for recommender systems"
    - "training and evaluating multimodal word embeddings with large scale web annotated images"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "visual search at pinterest"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-2 (induced by source paper "faster r cnn towards real time object detection with region proposal networks"):
  with members:
    - "what makes for effective detection proposals"
    - "object proposal evaluation protocol is gameable"
    - "learning to segment object candidates"
    - "deep sliding shapes for amodal 3d object detection in rgb d images"
    - "instance aware semantic segmentation via multi task network cascades"
    - "densecap fully convolutional localization networks for dense captioning"
    - "deepm a deep part based model for object detection and semantic part localization"
    - "deep residual learning for image recognition"

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)