human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "approximate query processing on autonomous cameras"):
  Abstract: Surveillance IoT cameras are becoming autonomous: they operate on batteries without connecting to power wires, and run analytics without help from external compute resources. Such autonomous paradigm significantly frees video analytics from privacy concern and deployment constraint. However, they are constrained by limited energy and on-device compute resources. #R##N#We present a system AutCam to support counting query, an important query class, on autonomous cameras. The system is built atop three key ideas. First, AutCam identifies and utilizes the rich trade-off between sample quality and sample quantity on a given time window. Second, AutCam introduces a split-phase sampling planner to judiciously allocate energy across multiple time windows, and also balance the capture and process energy. Third, AutCam constructs a unified bounded error by integrating the errors from both sampling and inaccurate neural networks. Tested on over 1,000-hour videos, our prototype of AutCam can provide good estimation of ground-truth object counts with bounded, narrow error under typical solar-harvested energy, and achieve up to 10X energy saving over baselines.

Includes members:
  Member "crowd counting with decomposed uncertainty":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "approximate query service on autonomous iot cameras")
  Member "scaling video analytics on constrained edge nodes":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "model assertions for monitoring and improving ml model")
  Member "vstore a data store for analytics on large videos":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "model assertions for monitoring and improving ml model")
  Member "supporting video queries on zero streaming cameras":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "approximate query service on autonomous iot cameras")
  Member "mobilenetv2 inverted residuals and linear bottlenecks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "yolov3 an incremental improvement":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "perception and decision making of autonomous systems in the era of learning an overview")
  Member "rexcam resource efficient cross camera video analytics at enterprise scale":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "approximate query service on autonomous iot cameras")
  Member "drone based object counting by spatially regularized regional proposal network":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "mini unmanned aerial vehicle based remote sensing techniques applications and prospects")

Question: Please tell me which class the source paper 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)