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: "multimedia communication quality assessment testbeds"
  Abstract: We make an intensive use of multimedia frameworks in our research on modeling the perceived quality estimation in streaming services and real-time communications. In our preliminary work, we have used the VLC VOD software to generate reference audiovisual files with various degree of coding and network degradations. We have successfully built machine learning based models on the subjective quality dataset we have generated using these files. However, imperfections in the dataset introduced by the multimedia framework we have used prevented us from achieving the full potential of these models. #R##N#In order to develop better models, we have re-created our end-to-end multimedia pipeline using the GStreamer framework for audio and video streaming. A GStreamer based pipeline proved to be significantly more robust to network degradations than the VLC VOD framework and allowed us to stream a video flow at a loss rate up to 5\% packet very easily. GStreamer has also enabled us to collect the relevant RTCP statistics that proved to be more accurate than network-deduced information. This dataset is free to the public. The accuracy of the statistics eventually helped us to generate better performing perceived quality estimation models. #R##N#In this paper, we present the implementation of these VLC and GStreamer-based multimedia communication quality assessment testbeds with the references to their publicly available code bases.

Connected through hyperedge HE-1 (induced by source paper "perceived audiovisual quality modelling based on decison trees genetic programming and neural networks"):
  with members:
    - "real time video quality representation classification of encrypted http adaptive video streaming the case of safari"

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)