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 rate adaptation algorithm for tile based 360 degree video streaming"
  Abstract: In the 360-degree immersive video, a user only views a part of the entire raw video frame based on her viewing direction. However, today's 360-degree video players always fetch the entire panoramic view regardless of users' head movement, leading to significant bandwidth waste that can be potentially avoided. In this paper, we propose a novel adaptive streaming scheme for 360-degree videos. The basic idea is to fetch the invisible portion of a video at the lowest quality based on users' head movement prediction and to adaptively decide the video playback quality for the visible portion based on bandwidth prediction. Doing both in a robust manner requires overcome a series of challenges, such as jointly considering the spatial and temporal domains, tolerating prediction errors, and achieving low complexity. To overcome these challenges, we first define quality of experience (QoE) metrics for adaptive 360-degree video streaming. We then formulate an optimization problem and solve it at a low complexity. The algorithm strategically leverages both future bandwidth and the distribution of users' head positions to determine the quality level of each tile (i.e., a sub-area of a raw frame). We further provide theoretical proof showing that our algorithm achieves optimality under practical assumptions. Numerical results show that our proposed algorithms significantly boost the user QoE by at least 20\% compared to baseline algorithms.

Connected through hyperedge HE-1 (induced by source paper "qoe driven coupled uplink and downlink rate adaptation for 360 degree video live streaming"):
  with members:
    - "optimal multicast of tiled 360 vr video"

Connected through hyperedge HE-2 (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"
    - "viewport adaptive navigable 360 degree video delivery"
    - "adam a method for stochastic optimization"
    - "probe and adapt rate adaptation for http video streaming at scale"

Connected through hyperedge HE-3 (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"
    - "streaming virtual reality content"
    - "adaptive 360 vr video streaming divide and conquer"

Connected through hyperedge HE-4 (induced by source paper "optimal multi quality multicast for 360 virtual reality video"):
  with members:
    - "optimal multicast of tiled 360 vr video"
    - "spatiotemporal rate adaptive tiled scheme for 360 sports events"

Connected through hyperedge HE-5 (induced by source paper "an algorithm for transmitting vr video based on adaptive modulation"):
  with members:
    - "virtual reality over wireless networks quality of service model and learning based resource management"

Connected through hyperedge HE-6 (induced by source paper "taming the latency in multi user vr 360 circ a qoe aware deep learning aided multicast framework"):
  with members:
    - "had you looked where i m looking cross user similarities in viewing behavior for 360 video and caching implications"
    - "ultra reliable communication in 5g mmwave networks a risk sensitive approach"
    - "communication computing and caching for mobile vr delivery modeling and trade off"
    - "machine learning for reliable mmwave systems blockage prediction and proactive handoff"
    - "edge computing meets millimeter wave enabled vr paving the way to cutting the cord"
    - "urllc embb slicing to support vr multimodal perceptions over wireless cellular systems"
    - "toward low latency and ultra reliable virtual reality"
    - "beyond wysiwyg sharing contextual sensing data through mmwave v2v communications"

Connected through hyperedge HE-7 (induced by source paper "qoe oriented resource allocation for 360 degree video transmission over heterogeneous networks"):
  with members:
    - "automatic salient object detection for panoramic images using region growing and fixation prediction model"
    - "salnet360 saliency maps for omni directional images with cnn"
    - "how do people explore virtual environments"
    - "bola near optimal bitrate adaptation for online videos"
    - "adaptive 360 vr video streaming divide and conquer"
    - "streaming video over http with consistent quality"
    - "non cooperative feedback rate control game for channel state information in wireless networks"

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)