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: "location recognition over large time lags"
  Abstract: Would it be possible to automatically associate ancient pictures to modern ones and create fancy cultural heritage city maps? We introduce here the task of recognizing the location depicted in an old photo given modern annotated images collected from the Internet. We present an extensive analysis on different features, looking for the most discriminative and most robust to the image variability induced by large time lags. Moreover, we show that the described task benefits from domain adaptation.

Connected through hyperedge HE-1 (induced by source paper "discovering visual patterns in art collections with spatially consistent feature learning"):
  with members:
    - "can computers create art"
    - "neighbourhood consensus networks"
    - "weakly supervised object detection in artworks"
    - "can creative adversarial networks generating art by learning about styles and deviating from style norms"
    - "fine tuning cnn image retrieval with no human annotation"
    - "bam the behance artistic media dataset for recognition beyond photography"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "omniart multi task deep learning for artistic data analysis"

Connected through hyperedge HE-2 (induced by source paper "cross domain image matching in presence of outliers"):
  with members:
    - "cross domain image matching with deep feature maps"
    - "adversarially learned one class classifier for novelty detection"
    - "importance weighted adversarial nets for partial domain adaptation"
    - "anomaly detection using one class neural networks"
    - "cross view image matching for geo localization in urban environments"
    - "deep hashing network for unsupervised domain adaptation"
    - "adversarial discriminative domain adaptation"
    - "asymmetric tri training for unsupervised domain adaptation"

Connected through hyperedge HE-3 (induced by source paper "leveraging outdoor webcams for local descriptor learning"):
  with members:
    - "matchable image retrieval by learning from surface reconstruction"
    - "from handcrafted to deep local invariant features"
    - "local descriptors optimized for average precision"
    - "learning a local feature descriptor for 3d lidar scans"
    - "structure from motion using dense cnn features with keypoint relocalization"
    - "a large dataset for improving patch matching"
    - "geodesc learning local descriptors by integrating geometry constraints"
    - "an analysis of deep neural network models for practical applications"

Connected through hyperedge HE-4 (induced by source paper "repeatability is not enough learning affine regions via discriminability"):
  with members:
    - "revisiting oxford and paris large scale image retrieval benchmarking"
    - "benchmarking 6dof urban visual localization in changing conditions"
    - "fine tuning cnn image retrieval with no human annotation"
    - "learning spread out local feature descriptors"
    - "hpatches a benchmark and evaluation of handcrafted and learned local descriptors"
    - "radially distorted conjugate translations"
    - "working hard to know your neighbor s margins local descriptor learning loss"
    - "systematic evaluation of convolution neural network advances on the imagenet"

Connected through hyperedge HE-5 (induced by source paper "midrank learning to rank based on subsequences"):
  with members:
    - "optimizing ranking measures for compact binary code learning"

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)