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: "clickbait click based accelerated incremental training of convolutional neural networks"
  Abstract: Today's general-purpose deep convolutional neural networks (CNN) for image classification and object detection are trained offline on large static datasets. Some applications, however, will require training in real-time on live video streams with a human-in-the-loop. We refer to this class of problem as Time-ordered Online Training (ToOT) - these problems will require a consideration of not only the quantity of incoming training data, but the human effort required to tag and use it. In this paper, we define training benefit as a metric to measure the effectiveness of a sequence in using each user interaction. We demonstrate and evaluate a system tailored to performing ToOT in the field, capable of training an image classifier on a live video stream through minimal input from a human operator. We show that by exploiting the time-ordered nature of the video stream through optical flow-based object tracking, we can increase the effectiveness of human actions by about 8 times.

Connected through hyperedge HE-1 (induced by source paper "querying zero streaming cameras"):
  with members:
    - "scaling video analytics on constrained edge nodes"
    - "vstore a data store for analytics on large videos"
    - "cornernet detecting objects as paired keypoints"
    - "secs efficient deep stream processing via class skew dichotomy"
    - "yolov3 an incremental improvement"
    - "blazeit fast exploratory video queries using neural networks"
    - "deeplens towards a visual data management system"
    - "scanner efficient video analysis at scale"

Connected through hyperedge HE-2 (induced by source paper "supporting video queries on zero streaming cameras"):
  with members:
    - "scaling video analytics on constrained edge nodes"
    - "vstore a data store for analytics on large videos"
    - "blazeit optimizing declarative aggregation and limit queries for neural network based video analytics"
    - "secs efficient deep stream processing via class skew dichotomy"
    - "yolov3 an incremental improvement"
    - "focus querying large video datasets with low latency and low cost"
    - "deeplens towards a visual data management system"
    - "rexcam resource efficient cross camera video analytics at enterprise scale"

Connected through hyperedge HE-3 (induced by source paper "semi supervised confidence network aided gated attention based recurrent neural network for clickbait detection"):
  with members:
    - "adversarial labeling for learning without labels"
    - "how curiosity can be modeled for a clickbait detector"
    - "clickbait detection in tweets using self attentive network"
    - "diving deep into clickbaits who use them to what extents in which topics with what effects"
    - "stop clickbait detecting and preventing clickbaits in online news media"
    - "we used neural networks to detect clickbaits you won t believe what happened next"
    - "show attend and tell neural image caption generation with visual attention"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-4 (induced by source paper "clickbait v2 training an object detector in real time"):
  with members:
    - "unsupervised learning from video to detect foreground objects in single images"
    - "training object class detectors with click supervision"
    - "ssd single shot multibox detector"
    - "click carving segmenting objects in video with point clicks"
    - "spot on action localization from pointly supervised proposals"
    - "deep interactive object selection"
    - "weakly supervised deep detection networks"
    - "adam a method for stochastic optimization"

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)