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 service on autonomous iot cameras"):
  Abstract: Today's analytics-powered cameras are still limited to urban, residential areas where power/network resources abound. To expand them to more diverse environments, especially those are off-grid and highly network-constrained, the cameras shall be ``autonomous'', i.e., independent from external power supply and compute infrastructure. Can autonomous cameras do any useful analytics? Our response is iCam, a runtime for autonomous cameras, that is capable of producing useful analytics results with frugal camera resources. iCam is built atop two novel techniques: (1) For the first time, iCam explores the joint tradeoff from both sampling strategy and wide selection of NN choices, and unifies the errors from both sources into one single bounded error. (2) Observing the disparate video characteristics across different time windows, iCam employs a learning-based planner that judiciously decides the sampling action at runtime. We test iCam with more than 1,000 hours of videos. On typical embedded hardware, iCam continuously generates object counts that are only within 11% of the true counts. Alongside the counts, iCam presents narrow error estimation which are shown to be bounded. It achieves up to 3.4x smaller bounded error over baselines.

Includes members:
  Member "crowd counting with decomposed uncertainty":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "counting and segmenting sorghum heads")
  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 processing on autonomous 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 "blazeit fast exploratory video queries using neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "video monitoring queries")
  Member "scanner efficient video analysis at scale":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "finding interesting frames in deep video analytics a top k approach")

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)