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: "camera identification with deep convolutional networks"
  Abstract: The possibility of detecting which camera has been used to shoot a specific picture is of paramount importance for many forensics tasks. This is extremely useful for copyright infringement cases, ownership attribution, as well as for detecting the authors of distributed illicit material (e.g., pedo-pornographic shots). Due to its importance, the forensics community has developed a series of robust detectors that exploit characteristic traces left by each camera on the acquired images during the acquisition pipeline. These traces are reverse-engineered in order to attribute a picture to a camera. In this paper, we investigate an alternative approach to solve camera identification problem. Indeed, we propose a data-driven algorithm based on convolutional neural networks, which learns features characterizing each camera directly from the acquired pictures. The proposed approach is tested on both instance-attribution and model-attribution, providing an accuracy greater than 94% in discriminating 27 camera models.

Connected through hyperedge HE-1 (induced by source paper "deep multi scale discriminative networks for double jpeg compression forensics"):
  with members:
    - "patch alignment manifold matting"
    - "clearing the skies a deep network architecture for single image rain removal"
    - "first steps toward camera model identification with convolutional neural networks"
    - "aligned and non aligned double jpeg detection using convolutional neural networks"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-2 (induced by source paper "deep learning for source camera identification on mobile devices"):
  with members:
    - "facenet a unified embedding for face recognition and clustering"
    - "very deep convolutional networks for large scale image recognition"
    - "visualizing and understanding convolutional networks"
    - "improving neural networks by preventing co adaptation of feature detectors"

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)