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: "multi level bottom top and top bottom feature fusion for crowd counting"
  Abstract: Crowd counting presents enormous challenges in the form of large variation in scales within images and across the dataset. These issues are further exacerbated in highly congested scenes. Approaches based on straightforward fusion of multi-scale features from a deep network seem to be obvious solutions to this problem. However, these fusion approaches do not yield significant improvements in the case of crowd counting in congested scenes. This is usually due to their limited abilities in effectively combining the multi-scale features for problems like crowd counting. To overcome this, we focus on how to efficiently leverage information present in different layers of the network. Specifically, we present a network that involves: (i) a multi-level bottom-top and top-bottom fusion (MBTTBF) method to combine information from shallower to deeper layers and vice versa at multiple levels, (ii) scale complementary feature extraction blocks (SCFB) involving cross-scale residual functions to explicitly enable flow of complementary features from adjacent conv layers along the fusion paths. Furthermore, in order to increase the effectiveness of the multi-scale fusion, we employ a principled way of generating scale-aware ground-truth density maps for training. Experiments conducted on three datasets that contain highly congested scenes (ShanghaiTech, UCF_CC_50, and UCF-QNRF) demonstrate that the proposed method is able to outperform several recent methods in all the datasets.

Connected through hyperedge HE-1 (induced by source paper "using depth for pixel wise detection of adversarial attacks in crowd counting"):
  with members:
    - "detecting the unexpected via image resynthesis"
    - "bayesian loss for crowd count estimation with point supervision"
    - "point in box out beyond counting persons in crowds"
    - "attacking optical flow"
    - "enforcing geometric constraints of virtual normal for depth prediction"
    - "learning spatial awareness to improve crowd counting"
    - "counting with focus for free"
    - "learn to scale generating multipolar normalized density map for crowd counting"

Connected through hyperedge HE-2 (induced by source paper "estimating people flows to better count them in crowded scenes"):
  with members:
    - "locality constrained spatial transformer network for video crowd counting"
    - "bayesian loss for crowd count estimation with point supervision"
    - "point in box out beyond counting persons in crowds"
    - "learning spatial awareness to improve crowd counting"
    - "counting with focus for free"
    - "learn to scale generating multipolar normalized density map for crowd counting"
    - "perspective guided convolution networks for crowd counting"
    - "learning from synthetic data for crowd counting in the wild"

Connected through hyperedge HE-3 (induced by source paper "feature aware adaptation and structured density alignment for crowd counting in video surveillance"):
  with members:
    - "bayesian uncertainty matching for unsupervised domain adaptation"
    - "dynamic region division for adaptive learning pedestrian counting"
    - "learning from synthetic data for crowd counting in the wild"
    - "pcc net perspective crowd counting via spatial convolutional network"
    - "crowd counting with deep structured scale integration network"
    - "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"
    - "learning to adapt structured output space for semantic segmentation"

Connected through hyperedge HE-4 (induced by source paper "segmentation guided attention network for crowd counting via curriculum learning"):
  with members:
    - "ha ccn hierarchical attention based crowd counting network"
    - "inverse attention guided deep crowd counting network"
    - "bayesian loss for crowd count estimation with point supervision"
    - "point in box out beyond counting persons in crowds"
    - "learning spatial awareness to improve crowd counting"
    - "counting with focus for free"
    - "improving the learning of multi column convolutional neural network for crowd counting"
    - "learning from synthetic data for crowd counting in the wild"

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)