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: "rexcam resource efficient cross camera video analytics at enterprise scale"
  Abstract: The deployment of large camera networks for video analytics is an established and accelerating trend. Many real video inference applications entail a common problem template: searching for an object or activity of interest (e.g. a person, a speeding vehicle) through a large camera network in live video. This capability, called cross-camera analytics, is compute and data intensive -- requiring automated search across cameras and across frames, at the throughput of the live video stream. To address the cost challenge of processing every raw video frame from a large deployment, we present ReXCam, a new system for efficient cross-camera video analytics. ReXCam exploits spatial and temporal locality in the dynamics of real camera networks to guide its inference-time search for a query identity. In an offline profiling phase, ReXCam builds a cross-camera correlation model that encodes the locality observed in historical traffic patterns. At inference time, ReXCam applies this model to filter frames that are not spatially and temporally correlated with the query identity's current position. In the cases of occasional missed detections, ReXCam performs a fast-replay search on recently filtered video frames, enabling gracefully recovery. Together, these techniques allow ReXCam to reduce compute workload by 4.6x and improve inference precision by 27% on a well-known video dataset with footage from eight cameras, while maintaining within 1-2% of baseline recall.

Connected through hyperedge HE-1 (induced by source paper "approximate query service on autonomous iot cameras"):
  with members:
    - "crowd counting with decomposed uncertainty"
    - "scaling video analytics on constrained edge nodes"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "edge intelligence paving the last mile of artificial intelligence with edge computing"):
  with members:
    - "machine learning at the wireless edge distributed stochastic gradient descent over the air"
    - "in edge ai intelligentizing mobile edge computing caching and communication by federated learning"
    - "peer to peer federated learning on graphs"
    - "pcamp performance comparison of machine learning packages on the edges"
    - "client selection for federated learning with heterogeneous resources in mobile edge"
    - "on device federated learning via blockchain and its latency analysis"
    - "federated learning strategies for improving communication efficiency"
    - "communication compression for decentralized training"

Connected through hyperedge HE-3 (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-4 (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"
    - "clickbait v2 training an object detector in real time"

Connected through hyperedge HE-5 (induced by source paper "approximate query processing on autonomous cameras"):
  with members:
    - "crowd counting with decomposed uncertainty"
    - "scaling video analytics on constrained edge nodes"
    - "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"
    - "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-6 (induced by source paper "a scalable framework for distributed object tracking across a many camera network"):
  with members:
    - "boxcars improving fine grained recognition of vehicles using 3 d bounding boxes in traffic surveillance"
    - "operator in the loop deep sequential multi camera feature fusion for person re identification"
    - "an analysis of deep neural network models for practical applications"
    - "big data and fog computing"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "yolo9000 better faster stronger"
    - "tensorflow a system for large scale machine learning"
    - "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.DC(Distributed, Parallel, and Cluster Computing)