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: "vision based real estate price estimation"
  Abstract: Since the advent of online real estate database companies like Zillow, Trulia and Redfin, the problem of automatic estimation of market values for houses has received considerable attention. Several real estate websites provide such estimates using a proprietary formula. Although these estimates are often close to the actual sale prices, in some cases they are highly inaccurate. One of the key factors that affects the value of a house is its interior and exterior appearance, which is not considered in calculating automatic value estimates. In this paper, we evaluate the impact of visual characteristics of a house on its market value. Using deep convolutional neural networks on a large dataset of photos of home interiors and exteriors, we develop a method for estimating the luxury level of real estate photos. We also develop a novel framework for automated value assessment using the above photos in addition to home characteristics including size, offered price and number of bedrooms. Finally, by applying our proposed method for price estimation to a new dataset of real estate photos and metadata, we show that it outperforms Zillow's estimates.

Connected through hyperedge HE-1 (induced by source paper "adapting grad cam for embedding networks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "hardness aware deep metric learning"
    - "visualizing how embeddings generalize"
    - "improved embeddings with easy positive triplet mining"
    - "divide and conquer the embedding space for metric learning"
    - "visual explanation for deep metric learning"
    - "visualizing deep similarity networks"
    - "deep metric learning beyond binary supervision"

Connected through hyperedge HE-2 (induced by source paper "a model for predicting price polarity of real estate properties using information of real estate market websites"):
  with members:
    - "w2vlda almost unsupervised system for aspect based sentiment analysis"
    - "xgboost a scalable tree boosting system"
    - "distributed representations of sentences and documents"
    - "word2vec explained deriving mikolov et al s negative sampling word embedding method"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-3 (induced by source paper "deep fundamental matrix estimation without correspondences"):
  with members:
    - "superpoint self supervised interest point detection and description"
    - "camera relocalization by predicting pairwise relative poses using convolutional neural network"
    - "convolutional neural network architecture for geometric matching"
    - "unsupervised deep homography a fast and robust homography estimation model"
    - "toward geometric deep slam"
    - "surfacenet an end to end 3d neural network for multiview stereopsis"
    - "deep 6 dof tracking"
    - "relative camera pose estimation using convolutional neural networks"

Connected through hyperedge HE-4 (induced by source paper "the economic value of neighborhoods predicting real estate prices from the urban environment"):
  with members:
    - "the new urban success how culture pays"
    - "image based appraisal of real estate properties"
    - "xgboost a scalable tree boosting system"
    - "the death and life of great italian cities a mobile phone data perspective"
    - "are safer looking neighborhoods more lively a multimodal investigation into urban life"
    - "why should i trust you explaining the predictions of any classifier"
    - "deep residual learning for image recognition"
    - "network lasso clustering and optimization in large graphs"

Connected through hyperedge HE-5 (induced by source paper "visual estimation of building condition with patch level convnets"):
  with members:
    - "automatic prediction of building age from photographs"
    - "house price prediction using lstm"
    - "house price estimation from visual and textual features"
    - "deep residual learning for image recognition"
    - "learning deep features for discriminative localization"
    - "matconvnet convolutional neural networks for matlab"

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)