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 "autoscale learning to scale for crowd counting"):
  Abstract: Crowd counting in images is a widely explored but challenging task. Though recent convolutional neural network (CNN) methods have achieved great progress, it is still difficult to accurately count and even to precisely localize people in very dense regions. A major issue is that dense regions usually consist of many instances of small size, and thus exhibit very different density patterns compared with sparse regions. Localizing or detecting dense small objects is also very delicate. In this paper, instead of processing image pyramid and aggregating multi-scale features, we propose a simple yet effective Learning to Scale (L2S) module to cope with significant scale variations in both regression and localization. Specifically, L2S module aims to automatically scale dense regions into similar and reasonable scale levels. This alleviates the density pattern shift for density regression methods and facilitates the localization of small instances. Besides, we also introduce a novel distance label map combined with a customized adapted cross-entropy loss for precise person localization. Extensive experiments demonstrate that the proposed method termed AutoScale consistently improves upon state-of-the-art methods in both regression and localization benchmarks on three widely used datasets. The proposed AutoScale also demonstrates a noteworthy transferability under cross-dataset validation on different datasets.

Includes members:
  Member "ha ccn hierarchical attention based crowd counting network":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multi level bottom top and top bottom feature fusion for crowd counting")
  Member "exploiting unlabeled data in cnns by self supervised learning to rank":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "nwpu crowd a large scale benchmark for crowd counting")
  Member "point in box out beyond counting persons in crowds":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "crowd scene analysis by output encoding")
  Member "learning from synthetic data for crowd counting in the wild":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "ha ccn hierarchical attention based crowd counting network")
  Member "divide and grow capturing huge diversity in crowd images with incrementally growing cnn":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "ha ccn hierarchical attention based crowd counting network")
  Member "composition loss for counting density map estimation and localization in dense crowds":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "ha ccn hierarchical attention based crowd counting network")
  Member "autofocus efficient multi scale inference":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "panda a gigapixel level human centric video dataset")
  Member "where are the blobs counting by localization with point supervision":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "crowd scene analysis by output encoding")

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)