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: "watch and learn semi supervised learning of object detectors from videos"
  Abstract: We present a semi-supervised approach that localizes multiple unknown object instances in long videos. We start with a handful of labeled boxes and iteratively learn and label hundreds of thousands of object instances. We propose criteria for reliable object detection and tracking for constraining the semi-supervised learning process and minimizing semantic drift. Our approach does not assume exhaustive labeling of each object instance in any single frame, or any explicit annotation of negative data. Working in such a generic setting allow us to tackle multiple object instances in video, many of which are static. In contrast, existing approaches either do not consider multiple object instances per video, or rely heavily on the motion of the objects present. The experiments demonstrate the effectiveness of our approach by evaluating the automatically labeled data on a variety of metrics like quality, coverage (recall), diversity, and relevance to training an object detector.

Connected through hyperedge HE-1 (induced by source paper "generalizable semi supervised learning method to estimate mass from sparsely annotated images"):
  with members:
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "semi supervised deep learning for monocular depth map prediction"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-2 (induced by source paper "4d generic video object proposals"):
  with members:
    - "mots multi object tracking and segmentation"
    - "bayesian semantic instance segmentation in open set world"
    - "zero shot object detection learning to simultaneously recognize and localize novel concepts"
    - "combined image and world space tracking in traffic scenes"
    - "instance embedding transfer to unsupervised video object segmentation"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "unsupervised hard example mining from videos for improved object detection"
    - "maskrnn instance level video object segmentation"

Connected through hyperedge HE-3 (induced by source paper "curriculum model adaptation with synthetic and real data for semantic foggy scene understanding"):
  with members:
    - "real time 3d traffic cone detection for autonomous driving"
    - "model adaptation with synthetic and real data for semantic dense foggy scene understanding"
    - "learning to adapt structured output space for semantic segmentation"
    - "semantic foggy scene understanding with synthetic data"
    - "domain adaptive faster r cnn for object detection in the wild"
    - "dark model adaptation semantic image segmentation from daytime to nighttime"
    - "end to end learning of driving models with surround view cameras and route planners"
    - "cycada cycle consistent adversarial domain adaptation"

Connected through hyperedge HE-4 (induced by source paper "mass estimation from images using deep neural network and sparse ground truth"):
  with members:
    - "semi supervised deep learning for monocular depth map prediction"
    - "identity mappings in deep residual networks"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-5 (induced by source paper "recycle gan unsupervised video retargeting"):
  with members:
    - "unsupervised video to video translation"
    - "multimodal unsupervised image to image translation"
    - "probabilistic video generation using holistic attribute control"
    - "learning to generate long term future via hierarchical prediction"
    - "cycada cycle consistent adversarial domain adaptation"
    - "pixelnn example based image synthesis"
    - "unsupervised learning of depth and ego motion from video"
    - "learning to discover cross domain relations with generative adversarial networks"

Connected through hyperedge HE-6 (induced by source paper "few shot object detection via feature reweighting"):
  with members:
    - "few example object detection with model communication"
    - "zero shot detection"
    - "zero shot object detection learning to simultaneously recognize and localize novel concepts"
    - "distilling critical paths in convolutional neural networks"
    - "yolov3 an incremental improvement"
    - "revisiting the importance of individual units in cnns via ablation"
    - "low shot learning from imaginary data"
    - "lstd a low shot transfer detector for object detection"

Connected through hyperedge HE-7 (induced by source paper "optimizing video object detection via a scale time lattice"):
  with members:
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "deep laplacian pyramid networks for fast and accurate super resolution"
    - "flow guided feature aggregation for video object detection"
    - "focal loss for dense object detection"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "not all pixels are equal difficulty aware semantic segmentation via deep layer cascade"
    - "detect to track and track to detect"
    - "dsod learning deeply supervised object detectors from scratch"

Connected through hyperedge HE-8 (induced by source paper "loans weakly supervised object detection with localizer assessor networks"):
  with members:
    - "sniper efficient multi scale training"
    - "mentornet learning data driven curriculum for very deep neural networks on corrupted labels"
    - "chainercv a library for deep learning in computer vision"
    - "learning from noisy labels with distillation"
    - "ssd single shot multibox detector"
    - "you only look once unified real time object detection"
    - "spatial transformer networks"
    - "deep residual learning for image recognition"

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)