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: "scaling video analytics on constrained edge nodes"
  Abstract: As video camera deployments continue to grow, the need to process large volumes of real-time data strains wide area network infrastructure. When per-camera bandwidth is limited, it is infeasible for applications such as traffic monitoring and pedestrian tracking to offload high-quality video streams to a datacenter. This paper presents FilterForward, a new edge-to-cloud system that enables datacenter-based applications to process content from thousands of cameras by installing lightweight edge filters that backhaul only relevant video frames. FilterForward introduces fast and expressive per-application microclassifiers that share computation to simultaneously detect dozens of events on computationally constrained edge nodes. Only matching events are transmitted to the cloud. Evaluation on two real-world camera feed datasets shows that FilterForward reduces bandwidth use by an order of magnitude while improving computational efficiency and event detection accuracy for challenging video content.

Connected through hyperedge HE-1 (induced by source paper "model assertions for monitoring and improving ml model"):
  with members:
    - "better ai through logical scaffolding"
    - "continuous integration of machine learning models with ease ml ci towards a rigorous yet practical treatment"
    - "outside the box abstraction based monitoring of neural networks"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "vstore a data store for analytics on large videos"
    - "selection via proxy efficient data selection for deep learning"
    - "formal security analysis of neural networks using symbolic intervals"
    - "formal verification of neural network controlled autonomous systems"

Connected through hyperedge HE-2 (induced by source paper "approximate query service on autonomous iot cameras"):
  with members:
    - "crowd counting with decomposed uncertainty"
    - "vstore a data store for analytics on large videos"
    - "supporting video queries on zero streaming cameras"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "yolov3 an incremental improvement"
    - "blazeit fast exploratory video queries using neural networks"
    - "scanner efficient video analysis at scale"
    - "rexcam resource efficient cross camera video analytics at enterprise scale"

Connected through hyperedge HE-3 (induced by source paper "querying zero streaming cameras"):
  with members:
    - "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"
    - "rexcam resource efficient cross camera video analytics at enterprise scale"

Connected through hyperedge HE-4 (induced by source paper "supporting video queries on zero streaming cameras"):
  with members:
    - "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"
    - "clickbait v2 training an object detector in real time"

Connected through hyperedge HE-5 (induced by source paper "hmm guided frame querying for bandwidth constrained video search"):
  with members:
    - "adascale towards real time video object detection using adaptive scaling"
    - "object detection in video with spatiotemporal sampling networks"
    - "towards high performance video object detection for mobiles"
    - "on calibration of modern neural networks"
    - "towards high performance video object detection"
    - "youtube boundingboxes a large high precision human annotated data set for object detection in video"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "approximate query processing on autonomous cameras"):
  with members:
    - "crowd counting with decomposed uncertainty"
    - "vstore a data store for analytics on large videos"
    - "supporting video queries on zero streaming cameras"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "yolov3 an incremental improvement"
    - "rexcam resource efficient cross camera video analytics at enterprise scale"
    - "drone based object counting by spatially regularized regional proposal network"
    - "automatically identifying counting and describing wild animals in camera trap images with deep learning"

Connected through hyperedge HE-7 (induced by source paper "blazeit optimizing declarative aggregation and limit queries for neural network based video analytics"):
  with members:
    - "vstore a data store for analytics on large videos"
    - "scanner efficient video analysis at scale"
    - "flow guided feature aggregation for video object detection"
    - "noscope optimizing neural network queries over video at scale"
    - "mask r cnn"
    - "eie efficient inference engine on compressed deep neural network"
    - "fast video classification via adaptive cascading of deep models"
    - "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)