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: "people counting in high density crowds from still images"
  Abstract: We present a method of estimating the number of people in high density crowds from still images. The method estimates counts by fusing information from multiple sources. Most of the existing work on crowd counting deals with very small crowds (tens of individuals) and use temporal information from videos. Our method uses only still images to estimate the counts in high density images (hundreds to thousands of individuals). At this scale, we cannot rely on only one set of features for count estimation. We, therefore, use multiple sources, viz. interest points (SIFT), Fourier analysis, wavelet decomposition, GLCM features and low confidence head detections, to estimate the counts. Each of these sources gives a separate estimate of the count along with confidences and other statistical measures which are then combined to obtain the final estimate. We test our method on an existing dataset of fifty images containing over 64000 individuals. Further, we added another fifty annotated images of crowds and tested on the complete dataset of hundred images containing over 87000 individuals. The counts per image range from 81 to 4633. We report the performance in terms of mean absolute error, which is a measure of accuracy of the method, and mean normalised absolute error, which is a measure of the robustness.

Connected through hyperedge HE-1 (induced by source paper "encoder decoder based convolutional neural networks with multi scale aware modules for crowd counting"):
  with members:
    - "bayesian loss for crowd count estimation with point supervision"
    - "dual path multi scale fusion networks with attention for crowd counting"
    - "c 3 framework an open source pytorch code for crowd counting"
    - "learning spatial awareness to improve crowd counting"
    - "learning from synthetic data for crowd counting in the wild"
    - "crowd counting with deep structured scale integration network"
    - "from open set to closed set counting objects by spatial divide and conquer"
    - "composition loss for counting density map estimation and localization in dense crowds"

Connected through hyperedge HE-2 (induced by source paper "cnn based cascaded multi task learning of high level prior and density estimation for crowd counting"):
  with members:
    - "resnetcrowd a residual deep learning architecture for crowd counting violent behaviour detection and crowd density level classification"
    - "hyperface a deep multi task learning framework for face detection landmark localization pose estimation and gender recognition"
    - "crowdnet a deep convolutional network for dense crowd counting"
    - "fully convolutional crowd counting on highly congested scenes"
    - "instance aware semantic segmentation via multi task network cascades"
    - "crowded scene analysis a survey"
    - "spatial pyramid pooling in deep convolutional networks for visual recognition"
    - "fully convolutional neural networks for crowd segmentation"

Connected through hyperedge HE-3 (induced by source paper "detecting violent and abnormal crowd activity using temporal analysis of grey level co occurrence matrix glcm based texture measures"):
  with members:
    - "holistic features for real time crowd behaviour anomaly detection"

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.CV(Computer Vision and Pattern Recognition)