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: "a hybrid approach to video source identification"
  Abstract: Multimedia Forensics allows to determine whether videos or images have been captured with the same device, and thus, eventually, by the same person. Currently, the most promising technology to achieve this task, exploits the unique traces left by the camera sensor into the visual content. Anyway, image and video source identification are still treated separately from one another. This approach is limited and anachronistic if we consider that most of the visual media are today acquired using smartphones, that capture both images and videos. In this paper we overcome this limitation by exploring a new approach that allows to synergistically exploit images and videos to study the device from which they both come. Indeed, we prove it is possible to identify the source of a digital video by exploiting a reference sensor pattern noise generated from still images taken by the same device of the query video. The proposed method provides comparable or even better performance, when compared to the current video identification strategies, where a reference pattern is estimated from video frames. We also show how this strategy can be effective even in case of in-camera digitally stabilized videos, where a non-stabilized reference is not available, by solving some state-of-the-art limitations. We explore a possible direct application of this result, that is social media profile linking, i.e. discovering relationships between two or more social media profiles by comparing the visual contents - images or videos - shared therein.

Connected through hyperedge HE-1 (induced by source paper "camera fingerprint extraction via spatial domain averaged frames"):
  with members:
    - "source camera attribution of multi format devices"
    - "facing device attribution problem for stabilized video sequences"

Connected through hyperedge HE-2 (induced by source paper "source camera attribution of multi format devices"):
  with members:
    - "facing device attribution problem for stabilized video sequences"

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)