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: "pushing the frontiers of unconstrained crowd counting new dataset and benchmark method"
  Abstract: In this work, we propose a novel crowd counting network that progressively generates crowd density maps via residual error estimation. The proposed method uses VGG16 as the backbone network and employs density map generated by the final layer as a coarse prediction to refine and generate finer density maps in a progressive fashion using residual learning. Additionally, the residual learning is guided by an uncertainty-based confidence weighting mechanism that permits the flow of only high-confidence residuals in the refinement path. The proposed Confidence Guided Deep Residual Counting Network (CG-DRCN) is evaluated on recent complex datasets, and it achieves significant improvements in errors. #R##N#Furthermore, we introduce a new large scale unconstrained crowd counting dataset (JHU-CROWD) that is ~2.8 larger than the most recent crowd counting datasets in terms of the number of images. It contains 4,250 images with 1.11 million annotations. In comparison to existing datasets, the proposed dataset is collected under a variety of diverse scenarios and environmental conditions. Specifically, the dataset includes several images with weather-based degradations and illumination variations in addition to many distractor images, making it a very challenging dataset. Additionally, the dataset consists of rich annotations at both image-level and head-level. Several recent methods are evaluated and compared on this dataset.

Connected through hyperedge HE-1 (induced by source paper "nwpu crowd a large scale benchmark for crowd counting"):
  with members:
    - "scar spatial channel wise attention regression networks for crowd counting"
    - "drone based joint density map estimation localization and tracking with space time multi scale attention network"
    - "exploiting unlabeled data in cnns by self supervised learning to rank"
    - "locality constrained spatial transformer network for video crowd counting"
    - "bayesian loss for crowd count estimation with point supervision"
    - "c 3 framework an open source pytorch code for crowd counting"
    - "the apolloscape open dataset for autonomous driving and its application"
    - "perspective guided convolution networks for crowd counting"

Connected through hyperedge HE-2 (induced by source paper "from open set to closed set supervised spatial divide and conquer for object counting"):
  with members:
    - "bayesian loss for crowd count estimation with point supervision"
    - "learning spatial awareness to improve crowd counting"
    - "learn to scale generating multipolar normalized density map for crowd counting"
    - "from open set to closed set counting objects by spatial divide and conquer"
    - "divide and grow capturing huge diversity in crowd images with incrementally growing cnn"
    - "composition loss for counting density map estimation and localization in dense crowds"
    - "crowd counting using deep recurrent spatial aware network"
    - "learning to adapt structured output space for semantic segmentation"

Connected through hyperedge HE-3 (induced by source paper "domain adaptive crowd counting via inter domain features segregation and gaussian prior reconstruction"):
  with members:
    - "c 3 framework an open source pytorch code for crowd counting"
    - "cross dataset person re identification via unsupervised pose disentanglement and adaptation"
    - "unsupervised domain specific deblurring via disentangled representations"
    - "all about structure adapting structural information across domains for boosting semantic segmentation"
    - "learning from synthetic data for crowd counting in the wild"
    - "crowd counting with deep structured scale integration network"
    - "generalizing semi supervised generative adversarial networks to regression using feature contrasting"
    - "divide and grow capturing huge diversity in crowd images with incrementally growing cnn"

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)