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 "cnn based estimation of abdominal circumference from ultrasound images"):
  Abstract: The obstetrics and gynecology ultrasound diagnosis is routinely used to check fetal biometry, and due to its time-consuming routine process, there has been great demand of automatic estimation. Automated analysis of ultrasound images is complicated because ultrasound images are patient-specific, operator-dependent, and machine specific. Among fetal biometry, abdominal circumference (AC) is more difficult to make accurate measurement automatically because abdomen has low contrast against surroundings, non-uniform contrast and irregular shape compared to other parameters. This paper proposes a framework for estimation of the fetal AC from 2D ultrasound data by a specially designed convolutional neural network (CNN) which takes account of doctors' decision process, anatomical structure, and the characteristics of ultrasound image. The proposed framework uses CNN to classify ultrasound images (stomach bubble, amniotic fluid, and umbilical vein) and the Hough transform for the measurement of the AC. We tested the proposed method using clinical ultrasound data acquired from 10 pregnant women. Experimental results showed that, with relatively small training samples, the proposed CNN provided sufficient classification results for AC estimation through the Hough transform. This framework showed good performance on most cases and even for ultrasound images deteriorated by shadowing artifacts. However, for oversized fetus cases, when amniotic fluid is not seen or abdominal area was distorted, it could not estimate correct AC.

Includes members:
  Member "adam a method for stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "caffe convolutional architecture for fast feature embedding":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "object detection in optical remote sensing images a survey and a new benchmark")

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.CV(Computer Vision and Pattern Recognition)