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: "vstore a data store for analytics on large videos"
  Abstract: We present VStore, a data store for supporting fast, resource-efficient analytics over large archival videos. VStore manages video ingestion, storage, retrieval, and consumption. It controls video formats along the video data path. It is challenged by i) the huge combinatorial space of video format knobs; ii) the complex impacts of these knobs and their high profiling cost; iii) optimizing for multiple resource types. It explores an idea called backward derivation of configuration: in the opposite direction along the video data path, VStore passes the video quantity and quality expected by analytics backward to retrieval, to storage, and to ingestion. In this process, VStore derives an optimal set of video formats, optimizing for different resources in a progressive manner.   VStore automatically derives large, complex configurations consisting of more than one hundred knobs over tens of video formats. In response to queries, VStore selects video formats catering to the executed operators and the target accuracy. It streams video data from disks through decoder to operators. It runs queries as fast as 362x of video realtime.

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"
    - "scaling video analytics on constrained edge nodes"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "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"
    - "scaling video analytics on constrained edge nodes"
    - "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 "grand challenges of resilience autonomous system resilience through design and runtime measures"):
  with members:
    - "spherical text embedding"
    - "straggler mitigation with tiered gradient codes"
    - "certified adversarial robustness via randomized smoothing"
    - "lower bounds on adversarial robustness from optimal transport"
    - "a reinforcement learning based approach for joint multi agent decision making"
    - "optimal server selection for straggler mitigation"
    - "formal security analysis of neural networks using symbolic intervals"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-4 (induced by source paper "querying zero streaming cameras"):
  with members:
    - "scaling video analytics on constrained edge nodes"
    - "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-5 (induced by source paper "supporting video queries on zero streaming cameras"):
  with members:
    - "scaling video analytics on constrained edge nodes"
    - "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-6 (induced by source paper "i safe instant suspicious activity identification at the edge using fuzzy decision making"):
  with members:
    - "decentralized smart surveillance through microservices platform"
    - "blendmas a blockchain enabled decentralized microservices architecture for smart public safety"
    - "real world anomaly detection in surveillance videos"
    - "focus querying large video datasets with low latency and low cost"
    - "kerman a hybrid lightweight tracking algorithm to enable smart surveillance as an edge service"
    - "real time index authentication for event oriented surveillance video query using blockchain"
    - "real time human detection as an edge service enabled by a lightweight cnn"
    - "posetrack a benchmark for human pose estimation and tracking"

Connected through hyperedge HE-7 (induced by source paper "approximate query processing on autonomous cameras"):
  with members:
    - "crowd counting with decomposed uncertainty"
    - "scaling video analytics on constrained edge nodes"
    - "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-8 (induced by source paper "blazeit optimizing declarative aggregation and limit queries for neural network based video analytics"):
  with members:
    - "scaling video analytics on constrained edge nodes"
    - "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.DB(Databases)