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: "viewport adaptive navigable 360 degree video delivery"
  Abstract: The delivery and display of 360-degree videos on Head-Mounted Displays (HMDs) presents many technical challenges. 360-degree videos are ultra high resolution spherical videos, which contain an omnidirectional view of the scene. However only a portion of this scene is displayed on the HMD. Moreover, HMD need to respond in 10 ms to head movements, which prevents the server to send only the displayed video part based on client feedback. To reduce the bandwidth waste, while still providing an immersive experience, a viewport-adaptive 360-degree video streaming system is proposed. The server prepares multiple video representations, which differ not only by their bit-rate, but also by the qualities of different scene regions. The client chooses a representation for the next segment such that its bit-rate fits the available throughput and a full quality region matches its viewing. We investigate the impact of various spherical-to-plane projections and quality arrangements on the video quality displayed to the user, showing that the cube map layout offers the best quality for the given bit-rate budget. An evaluation with a dataset of users navigating 360-degree videos demonstrates that segments need to be short enough to enable frequent view switches.

Connected through hyperedge HE-1 (induced by source paper "coco gan generation by parts via conditional coordinating"):
  with members:
    - "horizonnet learning room layout with 1d representation and pano stretch data augmentation"
    - "cgans with projection discriminator"
    - "introvae introspective variational autoencoders for photographic image synthesis"
    - "cube padding for weakly supervised saliency prediction in 360 deg videos"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "spectral normalization for generative adversarial networks"
    - "painting outside the box image outpainting with gans"
    - "escaping from collapsing modes in a constrained space"

Connected through hyperedge HE-2 (induced by source paper "slamcast large scale real time 3d reconstruction and streaming for immersive multi client live telepresence"):
  with members:
    - "collaborative large scale dense 3d reconstruction with online inter agent pose optimisation"
    - "a dynamic hash table for the gpu"
    - "adaptive 360 vr video streaming divide and conquer"
    - "bundlefusion real time globally consistent 3d reconstruction using on the fly surface re integration"

Connected through hyperedge HE-3 (induced by source paper "a taxonomy and dataset for 360 videos"):
  with members:
    - "spherical clustering of users navigating 360 degree content"
    - "predicting head movement in panoramic video a deep reinforcement learning approach"
    - "the prefetch aggressiveness tradeoff in 360 video streaming"
    - "movie editing and cognitive event segmentation in virtual reality video"

Connected through hyperedge HE-4 (induced by source paper "pano optimizing 360 deg video streaming with a better understanding of quality perception"):
  with members:
    - "yolov3 an incremental improvement"
    - "bola near optimal bitrate adaptation for online videos"
    - "adaptive 360 vr video streaming divide and conquer"
    - "high speed tracking with kernelized correlation filters"

Connected through hyperedge HE-5 (induced by source paper "probabilistic tile visibility based server side rate adaptation for adaptive 360 degree video streaming"):
  with members:
    - "predicting head movement in panoramic video a deep reinforcement learning approach"
    - "a rate adaptation algorithm for tile based 360 degree video streaming"
    - "adam a method for stochastic optimization"
    - "probe and adapt rate adaptation for http video streaming at scale"

Connected through hyperedge HE-6 (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"
    - "streaming virtual reality content"

Connected through hyperedge HE-7 (induced by source paper "spherical clustering of users navigating 360 degree content"):
  with members:
    - "predicting head movement in panoramic video a deep reinforcement learning approach"

Connected through hyperedge HE-8 (induced by source paper "accelerated structure aware reinforcement learning for delay sensitive energy harvesting wireless sensors"):
  with members:
    - "structural properties of optimal transmission policies for delay sensitive energy harvesting wireless sensors"
    - "aerial uav iot sensing for ubiquitous immersive communication and virtual human teleportation"
    - "dynamic spectrum access in cognitive radio networks with rf energy harvesting"
    - "a learning theoretic approach to energy harvesting communication system optimization"
    - "optimal energy allocation for wireless communications with energy harvesting constraints"
    - "fast reinforcement learning for energy efficient wireless communication"
    - "transmission with energy harvesting nodes in fading wireless channels optimal policies"
    - "optimal packet scheduling in an energy harvesting communication system"

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)