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: "beyond pixels image provenance analysis leveraging metadata"
  Abstract: Creative works, whether paintings or memes, follow unique journeys that result in their final form. Understanding these journeys, a process known as "provenance analysis", provides rich insights into the use, motivation, and authenticity underlying any given work. The application of this type of study to the expanse of unregulated content on the Internet is what we consider in this paper. Provenance analysis provides a snapshot of the chronology and validity of content as it is uploaded, re-uploaded, and modified over time. Although still in its infancy, automated provenance analysis for online multimedia is already being applied to different types of content. Most current works seek to build provenance graphs based on the shared content between images or videos. This can be a computationally expensive task, especially when considering the vast influx of content that the Internet sees every day. Utilizing non-content-based information, such as timestamps, geotags, and camera IDs can help provide important insights into the path a particular image or video has traveled during its time on the Internet without large computational overhead. This paper tests the scope and applicability of metadata-based inferences for provenance graph construction in two different scenarios: digital image forensics and cultural analytics.

Connected through hyperedge HE-1 (induced by source paper "learning transformation aware embeddings for image forensics"):
  with members:
    - "learning rich features for image manipulation detection"
    - "classification is a strong baseline for deep metric learning"
    - "fighting fake news image splice detection via learned self consistency"
    - "faceforensics a large scale video dataset for forgery detection in human faces"
    - "image processing operations identification via convolutional neural network"
    - "beyond triplet loss a deep quadruplet network for person re identification"
    - "a patchmatch based dense field algorithm for video copy move detection and localization"
    - "no fuss distance metric learning using proxies"

Connected through hyperedge HE-2 (induced by source paper "automatic discovery of political meme genres with diverse appearances"):
  with members:
    - "dynamic spatial verification for large scale object level image retrieval"
    - "dynamics of team library adoptions an exploration of github commit logs"
    - "needle in a haystack a framework for seeking small objects in big datasets"
    - "fighting fake news image splice detection via learned self consistency"
    - "secure detection of image manipulation by means of random feature selection"
    - "multimodal social media analysis for gang violence prevention"
    - "on the origins of memes by means of fringe web communities"
    - "memesequencer sparse matching for embedding image macros"

Connected through hyperedge HE-3 (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"
    - "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"
    - "learning spread out local feature descriptors"

Connected through hyperedge HE-4 (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"
    - "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"
    - "recent advance in content based image retrieval a literature survey"
    - "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-5 (induced by source paper "open issues in combating fake news interpretability as an opportunity"):
  with members:
    - "combating fake news a survey on identification and mitigation techniques"
    - "a survey of evaluation methods and measures for interpretable machine learning"
    - "fakenewsnet a data repository with news content social context and dynamic information for studying fake news on social media"
    - "declare debunking fake news and false claims using evidence aware deep learning"
    - "fake news a survey of research detection methods and opportunities"
    - "mitigating confirmation bias on twitter by recommending opposing views"
    - "beyond news contents the role of social context for fake news detection"
    - "some like it hoax automated fake news detection in social networks"

Connected through hyperedge HE-6 (induced by source paper "combating fake news with interpretable news feed algorithms"):
  with members:
    - "mesonet a compact facial video forgery detection network"
    - "fakenewsnet a data repository with news content social context and dynamic information for studying fake news on social media"
    - "detection and resolution of rumours in social media a survey"
    - "it s reducing a human being to a percentage perceptions of justice in algorithmic decisions"
    - "mitigating confirmation bias on twitter by recommending opposing views"
    - "quantifying search bias investigating sources of bias for political searches in social media"
    - "liar liar pants on fire a new benchmark dataset for fake news detection"
    - "a stylometric inquiry into hyperpartisan and fake news"

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)