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 systems to large camera deployments"
  Abstract: Driven by advances in computer vision and the falling costs of camera hardware, organizations are deploying video cameras en masse for the spatial monitoring of their physical premises. Scaling video analytics to massive camera deployments, however, presents a new and mounting challenge, as compute cost grows proportionally to the number of camera feeds. This paper is driven by a simple question: can we scale video analytics in such a way that cost grows sublinearly, or even remains constant, as we deploy more cameras, while inference accuracy remains stable, or even improves. We believe the answer is yes. Our key observation is that video feeds from wide-area camera deployments demonstrate significant content correlations (e.g. to other geographically proximate feeds), both in space and over time. These spatio-temporal correlations can be harnessed to dramatically reduce the size of the inference search space, decreasing both workload and false positive rates in multi-camera video analytics. By discussing use-cases and technical challenges, we propose a roadmap for scaling video analytics to large camera networks, and outline a plan for its realization.

Connected through hyperedge HE-1 (induced by source paper "convince collaborative cross camera video analytics at the edge"):
  with members:
    - "collaborative intelligent cross camera video analytics at edge opportunities and challenges"
    - "focus querying large video datasets with low latency and low cost"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "cyclegan a master of steganography"
    - "yolo9000 better faster stronger"

Connected through hyperedge HE-2 (induced by source paper "vstore a data store for analytics on large videos"):
  with members:
    - "person re identification by camera correlation aware feature augmentation"
    - "blazeit fast exploratory video queries using neural networks"
    - "vdms an efficient big visual data access for machine learning workloads"
    - "focus querying large video datasets with low latency and low cost"
    - "scanner efficient video analysis at scale"
    - "compressed video action recognition"
    - "noscope optimizing neural network queries over video at scale"
    - "fast video classification via adaptive cascading of deep models"

Connected through hyperedge HE-3 (induced by source paper "collaborative intelligent cross camera video analytics at edge opportunities and challenges"):
  with members:
    - "enabling cooperative inference of deep learning on wearables and smartphones"
    - "designing energy efficient convolutional neural networks using energy aware pruning"

Connected through hyperedge HE-4 (induced by source paper "rexcam resource efficient cross camera video analytics at scale"):
  with members:
    - "features for multi target multi camera tracking and re identification"
    - "nestdnn resource aware multi tenant on device deep learning for continuous mobile vision"
    - "large scale distributed neural network training through online distillation"
    - "focus querying large video datasets with low latency and low cost"
    - "object detection by spatio temporal analysis and tracking of the detected objects in a video with variable background"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "end to end flow correlation tracking with spatial temporal attention"
    - "noscope optimizing neural network queries over video at scale"

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)