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: "scalable nonlinear embeddings for semantic category based image retrieval"
  Abstract: We propose a novel algorithm for the task of supervised discriminative distance learning by nonlinearly embedding vectors into a low dimensional Euclidean space. We work in the challenging setting where supervision is with constraints on similar and dissimilar pairs while training. The proposed method is derived by an approximate kernelization of a linear Mahalanobis-like distance metric learning algorithm and can also be seen as a kernel neural network. The number of model parameters and test time evaluation complexity of the proposed method are O(dD) where D is the dimensionality of the input features and d is the dimension of the projection space - this is in contrast to the usual kernelization methods as, unlike them, the complexity does not scale linearly with the number of training examples. We propose a stochastic gradient based learning algorithm which makes the method scalable (w.r.t. the number of training examples), while being nonlinear. We train the method with up to half a million training pairs of 4096 dimensional CNN features. We give empirical comparisons with relevant baselines on seven challenging datasets for the task of low dimensional semantic category based image retrieval.

Connected through hyperedge HE-1 (induced by source paper "camera obscurer generative art for design inspiration"):
  with members:
    - "ann benchmarks a benchmarking tool for approximate nearest neighbor algorithms"
    - "web scale responsive visual search at bing"
    - "can creative adversarial networks generating art by learning about styles and deviating from style norms"
    - "focal loss for dense object detection"
    - "draw deep networks for recognizing styles of artists who illustrate children s books"
    - "mask r cnn"
    - "sift meets cnn a decade survey of instance retrieval"
    - "visual search at pinterest"

Connected through hyperedge HE-2 (induced by source paper "large scale novel object discovery in 3d"):
  with members:
    - "cp mtml coupled projection multi task metric learning for large scale face retrieval"
    - "deep sliding shapes for amodal 3d object detection in rgb d images"
    - "unsupervised learning of visual representations using videos"
    - "facenet a unified embedding for face recognition and clustering"
    - "multimodal deep learning for robust rgb d object recognition"
    - "3d shapenets a deep representation for volumetric shapes"
    - "learning rich features from rgb d images for object detection and segmentation"
    - "rotational projection statistics for 3d local surface description and object recognition"

Connected through hyperedge HE-3 (induced by source paper "embedding label structures for fine grained feature representation"):
  with members:
    - "a large scale car dataset for fine grained categorization and verification"
    - "simultaneous feature learning and hash coding with deep neural networks"
    - "deep feature learning with relative distance comparison for person re identification"
    - "bilinear cnn models for fine grained visual recognition"
    - "facenet a unified embedding for face recognition and clustering"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "part based r cnns for fine grained category detection"

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)