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: "streaming virtual reality content"
  Abstract: The recent rise of interest in Virtual Reality (VR) came with the availability of commodity commercial VR prod- ucts, such as the Head Mounted Displays (HMD) created by Oculus and other vendors. To accelerate the user adoption of VR headsets, content providers should focus on producing high quality immersive content for these devices. Similarly, multimedia streaming service providers should enable the means to stream 360 VR content on their platforms. In this study, we try to cover different aspects related to VR content representation, streaming, and quality assessment that will help establishing the basic knowledge of how to build a VR streaming system.

Connected through hyperedge HE-1 (induced by source paper "visual question answering on 360 deg images"):
  with members:
    - "towards vqa models that can read"
    - "uniter learning universal image text representations"
    - "vilbert pretraining task agnostic visiolinguistic representations for vision and language tasks"
    - "visualbert a simple and performant baseline for vision and language"
    - "vl bert pre training of generic visual linguistic representations"
    - "360 indoor towards learning real world objects in 360 deg indoor equirectangular images"
    - "touchdown natural language navigation and spatial reasoning in visual street environments"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-2 (induced by source paper "visual distortions in 360 degree videos"):
  with members:
    - "bridge the gap between vqa and human behavior on omnidirectional video a large scale dataset and a deep learning model"
    - "estimation of optimal encoding ladders for tiled 360 deg vr video in adaptive streaming systems"
    - "a rate adaptation algorithm for tile based 360 degree video streaming"
    - "adaptive 360 vr video streaming divide and conquer"

Connected through hyperedge HE-3 (induced by source paper "increasing the quality of 360 video streaming by transitioning between viewport quality adaptation mechanisms"):
  with members:
    - "viewport driven rate distortion optimized 360 deg video streaming"
    - "viewport adaptive navigable 360 degree video delivery"

Connected through hyperedge HE-4 (induced by source paper "video multimethod assessment fusion vmaf on 360vr contents"):
  with members:
    - "learning to predict streaming video qoe distortions rebuffering and memory"

Connected through hyperedge HE-5 (induced by source paper "methodology for accurately assessing the quality perceived by users on 360vr contents"):
  with members:
    - "visual distortions in 360 degree videos"

Connected through hyperedge HE-6 (induced by source paper "communications caching and computing for mobile virtual reality modeling and tradeoff"):
  with members:
    - "communication computing and caching for mobile vr delivery modeling and trade off"
    - "edge computing meets millimeter wave enabled vr paving the way to cutting the cord"
    - "joint communication computation caching and control in big data multi access edge computing"
    - "a survey on mobile edge computing the communication perspective"
    - "virtual reality over wireless networks quality of service model and learning based resource management"
    - "the three primary colors of mobile systems"
    - "power delay tradeoff in multi user mobile edge computing systems"
    - "on content centric wireless delivery networks"

Connected through hyperedge HE-7 (induced by source paper "a robust algorithm for tile based 360 degree video streaming with uncertain fov estimation"):
  with members:
    - "adaptive 360 vr video streaming divide and conquer"
    - "online convex optimization using predictions"

Connected through hyperedge HE-8 (induced by source paper "a rate adaptation algorithm for tile based 360 degree video streaming"):
  with members:
    - "adaptive 360 vr video streaming divide and conquer"

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)