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: "learning concept embeddings with combined human machine expertise"
  Abstract: This paper presents our work on "SNaCK," a low-dimensional concept embedding algorithm that combines human expertise with automatic machine similarity kernels. Both parts are complimentary: human insight can capture relationships that are not apparent from the object's visual similarity and the machine can help relieve the human from having to exhaustively specify many constraints. We show that our SNaCK embeddings are useful in several tasks: distinguishing prime and nonprime numbers on MNIST, discovering labeling mistakes in the Caltech UCSD Birds (CUB) dataset with the help of deep-learned features, creating training datasets for bird classifiers, capturing subjective human taste on a new dataset of 10,000 foods, and qualitatively exploring an unstructured set of pictographic characters. Comparisons with the state-of-the-art in these tasks show that SNaCK produces better concept embeddings that require less human supervision than the leading methods.

Connected through hyperedge HE-1 (induced by source paper "probabilistic end to end noise correction for learning with noisy labels"):
  with members:
    - "deep learning is robust to massive label noise"
    - "joint optimization framework for learning with noisy labels"
    - "dimensionality driven learning with noisy labels"
    - "generalized cross entropy loss for training deep neural networks with noisy labels"
    - "iterative learning with open set noisy labels"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "deep label distribution learning with label ambiguity"
    - "cleannet transfer learning for scalable image classifier training with label noise"

Connected through hyperedge HE-2 (induced by source paper "theme matters fashion compatibility learning via theme attention"):
  with members:
    - "fashion outfit generation for e commerce"
    - "learning type aware embeddings for fashion compatibility"
    - "recommending outfits from personal closet"
    - "outfit generation and style extraction via bidirectional lstm and autoencoder"
    - "creating capsule wardrobes from fashion images"
    - "compatibility family learning for item recommendation and generation"
    - "learning the latent look unsupervised discovery of a style coherent embedding from fashion images"
    - "learning fashion compatibility with bidirectional lstms"

Connected through hyperedge HE-3 (induced by source paper "fast stochastic ordinal embedding with variance reduction and adaptive step size"):
  with members:
    - "crowdsourced correlation clustering with relative distance comparisons"
    - "multi scale orderless pooling of deep convolutional activation features"
    - "cost effective hits for relative similarity comparisons"
    - "active ranking using pairwise comparisons"
    - "adaptively learning the crowd kernel"
    - "learning multi modal similarity"
    - "an active learning algorithm for ranking from pairwise preferences with an almost optimal query complexity"

Connected through hyperedge HE-4 (induced by source paper "multi attention multi class constraint for fine grained image recognition"):
  with members:
    - "generalized orderless pooling performs implicit salient matching"
    - "deep metric learning with angular loss"
    - "squeeze and excitation networks"
    - "diversified visual attention networks for fine grained object classification"
    - "residual attention network for image classification"
    - "the inaturalist species classification and detection dataset"
    - "dynamic computational time for visual attention"
    - "soft proposal networks for weakly supervised object localization"

Connected through hyperedge HE-5 (induced by source paper "less but better generalization enhancement of ordinal embedding via distributional margin"):
  with members:
    - "optimal margin distribution machine"
    - "efficient online relative comparison kernel learning"
    - "cost effective hits for relative similarity comparisons"
    - "large margin distribution machine"
    - "adaptively learning the crowd kernel"
    - "on the doubt about margin explanation of boosting"

Connected through hyperedge HE-6 (induced by source paper "integrating scene text and visual appearance for fine grained image classification"):
  with members:
    - "attention based multimodal fusion for video description"
    - "text flow a unified text detection system in natural scene images"
    - "image captioning with semantic attention"
    - "textproposals a text specific selective search algorithm for word spotting in the wild"
    - "ssd single shot multibox detector"
    - "weakly supervised fine grained categorization with part based image representation"
    - "synthetic data for text localisation in natural images"
    - "textboxes a fast text detector with a single deep neural network"

Connected through hyperedge HE-7 (induced by source paper "criteria sliders learning continuous database criteria via interactive ranking"):
  with members:
    - "building statistical shape spaces for 3d human modeling"
    - "active sampling of pairs and points for large scale linear bipartite ranking"
    - "local high order regularization on data manifolds"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-8 (induced by source paper "conditional similarity networks"):
  with members:
    - "infogan interpretable representation learning by information maximizing generative adversarial nets"
    - "multi view convolutional neural networks for 3d shape recognition"
    - "bilinear cnn models for fine grained visual recognition"
    - "deep residual learning for image recognition"
    - "facenet a unified embedding for face recognition and clustering"
    - "draw a recurrent neural network for image generation"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale 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)