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: "spotting the difference context retrieval and analysis for improved forgery detection and localization"
  Abstract: As image tampering becomes ever more sophisticated and commonplace, the need for image forensics algorithms that can accurately and quickly detect forgeries grows. In this paper, we revisit the ideas of image querying and retrieval to provide clues to better localize forgeries. We propose a method to perform large-scale image forensics on the order of one million images using the help of an image search algorithm and database to gather contextual clues as to where tampering may have taken place. In this vein, we introduce five new strongly invariant image comparison methods and test their effectiveness under heavy noise, rotation, and color space changes. Lastly, we show the effectiveness of these methods compared to passive image forensics using Nimble [this https URL], a new, state-of-the-art dataset from the National Institute of Standards and Technology (NIST).

Connected through hyperedge HE-1 (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"
    - "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-2 (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"
    - "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-3 (induced by source paper "beyond pixels image provenance analysis leveraging metadata"):
  with members:
    - "image provenance analysis at scale"
    - "fighting fake news image splice detection via learned self consistency"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "anti forensics of camera identification and the triangle test by improved fingerprint copy attack"
    - "provenance filtering for multimedia phylogeny"
    - "u phylogeny undirected provenance graph construction in the wild"
    - "a neural algorithm of artistic style"
    - "learning to compare image patches via convolutional neural networks"

Connected through hyperedge HE-4 (induced by source paper "image provenance analysis at scale"):
  with members:
    - "billion scale similarity search with gpus"
    - "u phylogeny undirected provenance graph construction in the wild"
    - "visual object tracking performance measures revisited"
    - "going deeper with convolutions"

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)