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: "visual search at pinterest"
  Abstract: We demonstrate that, with the availability of distributed computation platforms such as Amazon Web Services and open-source tools, it is possible for a small engineering team to build, launch and maintain a cost-effective, large-scale visual search system with widely available tools. We also demonstrate, through a comprehensive set of live experiments at Pinterest, that content recommendation powered by visual search improve user engagement. By sharing our implementation details and the experiences learned from launching a commercial visual search engines from scratch, we hope visual search are more widely incorporated into today's commercial applications.

Connected through hyperedge HE-1 (induced by source paper "quantifying engagement with citations on wikipedia"):
  with members:
    - "citation needed a taxonomy and algorithmic assessment of wikipedia s verifiability"
    - "mapping the backbone of the humanities through the eyes of wikipedia"
    - "detecting and gauging impact on wikipedia page views"
    - "eliciting new wikipedia users interests via automatically mined questionnaires for a warm welcome not a cold start"
    - "why the world reads wikipedia beyond english speakers"
    - "structuring wikipedia articles with section recommendations"
    - "amplifying the impact of open access wikipedia and the diffusion of science"
    - "why we read wikipedia"

Connected through hyperedge HE-2 (induced by source paper "learning a unified embedding for visual search at pinterest"):
  with members:
    - "classification is a strong baseline for deep metric learning"
    - "graph convolutional neural networks for web scale recommender systems"
    - "group normalization"
    - "a modulation module for multi task learning with applications in image retrieval"
    - "taskonomy disentangling task transfer learning"
    - "web scale responsive visual search at bing"
    - "sampling matters in deep embedding learning"
    - "visual search at ebay"

Connected through hyperedge HE-3 (induced by source paper "seeker real time interactive search"):
  with members:
    - "building a conversational agent overnight with dialogue self play"
    - "deep contextualized word representations"
    - "diversifying music recommendations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "web scale responsive visual search at bing"
    - "flowqa grasping flow in history for conversational machine comprehension"
    - "boltzmann exploration done right"
    - "a tutorial on thompson sampling"

Connected through hyperedge HE-4 (induced by source paper "unbiased evaluation of deep metric learning algorithms"):
  with members:
    - "ranked list loss for deep metric learning"
    - "few shot learning with embedded class models and shot free meta training"
    - "deep metric learning with bier boosting independent embeddings robustly"
    - "deep randomized ensembles for metric learning"
    - "deep metric learning with hierarchical triplet loss"
    - "attention based ensemble for deep metric learning"
    - "scalable logo recognition using proxies"
    - "prototypical networks for few shot learning"

Connected through hyperedge HE-5 (induced by source paper "infaas a model less inference serving system"):
  with members:
    - "one size does not fit all quantifying and exposing the accuracy latency trade off in machine learning cloud service apis via tolerance tiers"
    - "dynamic space time scheduling for gpu inference"
    - "salus fine grained gpu sharing primitives for deep learning applications"
    - "pretzel opening the black box of machine learning prediction serving systems"
    - "trims transparent and isolated model sharing for low latency deep learninginference in function as a service environments"
    - "tvm an automated end to end optimizing compiler for deep learning"
    - "lit block wise intermediate representation training for model compression"
    - "scanner efficient video analysis at scale"

Connected through hyperedge HE-6 (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"
    - "scalable nonlinear embeddings for semantic category based image retrieval"

Connected through hyperedge HE-7 (induced by source paper "texture retrieval in the wild through detection based attributes"):
  with members:
    - "texel att representing and classifying element based textures by attributes"
    - "a survey of recent advances in texture representation"
    - "training deep networks with synthetic data bridging the reality gap by domain randomization"
    - "looking beyond appearances synthetic training data for deep cnns in re identification"
    - "visual search at ebay"
    - "vnect real time 3d human pose estimation with a single rgb camera"
    - "mask r cnn"
    - "whittlesearch interactive image search with relative attribute feedback"

Connected through hyperedge HE-8 (induced by source paper "visual search over billions of aerial and satellite images"):
  with members:
    - "generalization in metric learning should the embedding layer be the embedding layer"
    - "visual discovery at pinterest"
    - "good practice in cnn feature transfer"
    - "sift meets cnn a decade survey of instance retrieval"
    - "data intensive supercomputing in the cloud global analytics for satellite imagery"
    - "deep residual learning for image recognition"
    - "particular object retrieval with integral max pooling of cnn activations"
    - "neural codes for image retrieval"

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)