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: "recent advance in content based image retrieval a literature survey"
  Abstract: The explosive increase and ubiquitous accessibility of visual data on the Web have led to the prosperity of research activity in image search or retrieval. With the ignorance of visual content as a ranking clue, methods with text search techniques for visual retrieval may suffer inconsistency between the text words and visual content. Content-based image retrieval (CBIR), which makes use of the representation of visual content to identify relevant images, has attracted sustained attention in recent two decades. Such a problem is challenging due to the intention gap and the semantic gap problems. Numerous techniques have been developed for content-based image retrieval in the last decade. The purpose of this paper is to categorize and evaluate those algorithms proposed during the period of 2003 to 2016. We conclude with several promising directions for future research.

Connected through hyperedge HE-1 (induced by source paper "machine learning based co creative design framework"):
  with members:
    - "friend collaborator student manager how design of an ai driven game level editor affects creators"
    - "toward a neuro inspired creative decoder"
    - "design design inspiration from generative networks"
    - "co creative level design via machine learning"
    - "outfit generation and style extraction via bidirectional lstm and autoencoder"
    - "styling with attention to details"
    - "generative image inpainting with contextual attention"
    - "can creative adversarial networks generating art by learning about styles and deviating from style norms"

Connected through hyperedge HE-2 (induced by source paper "deep active learning for biased datasets via fisher kernel self supervision"):
  with members:
    - "remap multi layer entropy guided pooling of dense cnn features for image retrieval"
    - "variational adversarial active learning"
    - "data efficient image recognition with contrastive predictive coding"
    - "learning representations by maximizing mutual information across views"
    - "interpreting black box predictions using fisher kernels"
    - "unsupervised representation learning by predicting image rotations"
    - "learning to reweight examples for robust deep learning"
    - "explaining nonlinear classification decisions with deep taylor decomposition"

Connected through hyperedge HE-3 (induced by source paper "cbir using features derived by deep learning"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "an analysis of deep neural network models for practical applications"
    - "learning transferable architectures for scalable image recognition"
    - "unsupervised part based weighting aggregation of deep convolutional features for image retrieval"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "densely connected convolutional networks"
    - "xception deep learning with depthwise separable convolutions"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-4 (induced by source paper "show match and segment joint learning of semantic matching and object co segmentation"):
  with members:
    - "semi supervised semantic matching"
    - "feelvos fast end to end embedding learning for video object segmentation"
    - "semantic aware attention based deep object co segmentation"
    - "recurrent transformer networks for semantic correspondence"
    - "df net unsupervised joint learning of depth and flow using cross task consistency"
    - "deepmvs learning multi view stereopsis"
    - "self supervised learning of geometrically stable features through probabilistic introspection"
    - "multimodal unsupervised image to image translation"

Connected through hyperedge HE-5 (induced by source paper "needle in a haystack a framework for seeking small objects in big datasets"):
  with members:
    - "discovering visual patterns in art collections with spatially consistent feature learning"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "beyond pixels image provenance analysis leveraging metadata"
    - "heated up softmax embedding"
    - "spotting the difference context retrieval and analysis for improved forgery detection and localization"
    - "billion scale similarity search with gpus"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "region based image retrieval revisited"

Connected through hyperedge HE-6 (induced by source paper "benchmarking unsupervised near duplicate image detection"):
  with members:
    - "an anti fraud system for car insurance claim based on visual evidence"
    - "billion scale similarity search with gpus"
    - "saliency weighted convolutional features for instance search"
    - "end to end learning of deep visual representations for image retrieval"
    - "deep image retrieval learning global representations for image search"
    - "sift meets cnn a decade survey of instance retrieval"
    - "cross dimensional weighting for aggregated deep convolutional features"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-7 (induced by source paper "dynamic spatial verification for large scale object level image retrieval"):
  with members:
    - "discovering visual patterns in art collections with spatially consistent feature learning"
    - "beyond pixels image provenance analysis leveraging metadata"
    - "spotting the difference context retrieval and analysis for improved forgery detection and localization"
    - "billion scale similarity search with gpus"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "lift learned invariant feature transform"
    - "deep image retrieval learning global representations for image search"
    - "large scale image retrieval with attentive deep local features"

Connected through hyperedge HE-8 (induced by source paper "unsupervised feature learning in remote sensing"):
  with members:
    - "reduced focal loss 1st place solution to xview object detection in satellite imagery"
    - "xview objects in context in overhead imagery"
    - "focal loss for dense object detection"
    - "microsoft coco common objects in context"

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.MM(Multimedia)