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: "online model distillation for efficient video inference"
  Abstract: High-quality computer vision models typically address the problem of understanding the general distribution of real-world images. However, most cameras observe only a very small fraction of this distribution. This offers the possibility of achieving more efficient inference by specializing compact, low-cost models to the specific distribution of frames observed by a single camera. In this paper, we employ the technique of model distillation (supervising a low-cost student model using the output of a high-cost teacher) to specialize accurate, low-cost semantic segmentation models to a target video stream. Rather than learn a specialized student model on offline data from the video stream, we train the student in an online fashion on the live video, intermittently running the teacher to provide a target for learning. Online model distillation yields semantic segmentation models that closely approximate their Mask R-CNN teacher with 7 to 17$\times$ lower inference runtime cost (11 to 26$\times$ in FLOPs), even when the target video's distribution is non-stationary. Our method requires no offline pretraining on the target video stream, achieves higher accuracy and lower cost than solutions based on flow or video object segmentation, and can exhibit better temporal stability than the original teacher. We also provide a new video dataset for evaluating the efficiency of inference over long running video streams.

Connected through hyperedge HE-1 (induced by source paper "tkd temporal knowledge distillation for active perception"):
  with members:
    - "a novel design of adaptive and hierarchical convolutional neural networks using partial reconfiguration on fpga"
    - "real world anomaly detection in surveillance videos"
    - "object detection at 200 frames per second"
    - "yolov3 an incremental improvement"
    - "gaple generalizable approaching policy learning for robotic object searching in indoor environment"
    - "domain adaptive faster r cnn for object detection in the wild"
    - "dark model adaptation semantic image segmentation from daytime to nighttime"
    - "active object perceiver recognition guided policy learning for object searching on mobile robots"

Connected through hyperedge HE-2 (induced by source paper "side tuning network adaptation via additive side networks"):
  with members:
    - "on the variance of the adaptive learning rate and beyond"
    - "rapid learning or feature reuse towards understanding the effectiveness of maml"
    - "superposition of many models into one"
    - "habitat a platform for embodied ai research"
    - "residual policy learning"
    - "taskonomy disentangling task transfer learning"
    - "efficient lifelong learning with a gem"
    - "know what you don t know unanswerable questions for squad"

Connected through hyperedge HE-3 (induced by source paper "dataset culling towards efficient training of distillation based domain specific models"):
  with members:
    - "focal loss for dense object detection"
    - "cost effective active learning for deep image classification"
    - "data distillation towards omni supervised learning"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "noscope optimizing neural network queries over video at scale"
    - "ssd single shot multibox detector"
    - "training region based object detectors with online hard example mining"
    - "you only look once unified real time object detection"

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.CV(Computer Vision and Pattern Recognition)