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 penguins and petri dishes adapting object counting models to new visual domains and object types without forgetting"
  Abstract: In this paper we propose a technique to adapt a convolutional neural network (CNN) based object counter to additional visual domains and object types while still preserving the original counting function. Domain-specific normalisation and scaling operators are trained to allow the model to adjust to the statistical distributions of the various visual domains. The developed adaptation technique is used to produce a singular patch-based counting regressor capable of counting various object types including people, vehicles, cell nuclei and wildlife. As part of this study a challenging new cell counting dataset in the context of tissue culture and patient diagnosis is constructed. This new collection, referred to as the Dublin Cell Counting (DCC) dataset, is the first of its kind to be made available to the wider computer vision community. State-of-the-art object counting performance is achieved in both the Shanghaitech (parts A and B) and Penguins datasets while competitive performance is observed on the TRANCOS and Modified Bone Marrow (MBM) datasets, all using a shared counting model.

Connected through hyperedge HE-1 (induced by source paper "pushing the frontiers of unconstrained crowd counting new dataset and benchmark method"):
  with members:
    - "ha ccn hierarchical attention based crowd counting network"
    - "inverse attention guided deep crowd counting network"
    - "c 3 framework an open source pytorch code for crowd counting"
    - "crowd counting and density estimation by trellis encoder decoder network"
    - "dafe fd density aware feature enrichment for face detection"
    - "learning from synthetic data for crowd counting in the wild"
    - "uncertainty guided multi scale residual learning using a cycle spinning cnn for single image de raining"
    - "divide and grow capturing huge diversity in crowd images with incrementally growing cnn"

Connected through hyperedge HE-2 (induced by source paper "exploiting unlabeled data in cnns by self supervised learning to rank"):
  with members:
    - "composition loss for counting density map estimation and localization in dense crowds"
    - "where are the blobs counting by localization with point supervision"
    - "leveraging unlabeled data for crowd counting by learning to rank"
    - "csrnet dilated convolutional neural networks for understanding the highly congested scenes"
    - "iterative crowd counting"
    - "on the use of deep learning for blind image quality assessment"
    - "switching convolutional neural network for crowd counting"
    - "decidenet counting varying density crowds through attention guided detection and density estimation"

Connected through hyperedge HE-3 (induced by source paper "deep built structure counting in satellite imagery using attention based re weighting"):
  with members:
    - "xview objects in context in overhead imagery"
    - "exploring models and data for remote sensing image caption generation"
    - "patternnet a benchmark dataset for performance evaluation of remote sensing image retrieval"
    - "detecting and counting tiny faces"
    - "leveraging unlabeled data for crowd counting by learning to rank"
    - "deepglobe 2018 a challenge to parse the earth through satellite images"
    - "switching convolutional neural network for crowd counting"
    - "decidenet counting varying density crowds through attention guided detection and density estimation"

Connected through hyperedge HE-4 (induced by source paper "counting with focus for free"):
  with members:
    - "crowd counting using scale aware attention networks"
    - "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"
    - "where are the blobs counting by localization with point supervision"
    - "leveraging unlabeled data for crowd counting by learning to rank"
    - "csrnet dilated convolutional neural networks for understanding the highly congested scenes"
    - "iterative crowd counting"

Connected through hyperedge HE-5 (induced by source paper "inverse attention guided deep crowd counting network"):
  with members:
    - "ha ccn hierarchical attention based crowd counting network"
    - "dafe fd density aware feature enrichment for face detection"
    - "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"
    - "leveraging unlabeled data for crowd counting by learning to rank"
    - "top down feedback for crowd counting convolutional neural network"
    - "csrnet dilated convolutional neural networks for understanding the highly congested scenes"
    - "reverse attention for salient object detection"

Connected through hyperedge HE-6 (induced by source paper "bayesian loss for crowd count estimation with point supervision"):
  with members:
    - "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"
    - "occlusion aware r cnn detecting pedestrians in a crowd"
    - "where are the blobs counting by localization with point supervision"
    - "leveraging unlabeled data for crowd counting by learning to rank"
    - "iterative crowd counting"
    - "an aggregated multicolumn dilated convolution network for perspective free counting"
    - "switching convolutional neural network for crowd counting"

Connected through hyperedge HE-7 (induced by source paper "truck traffic monitoring with satellite images"):
  with members:
    - "xview objects in context in overhead imagery"
    - "a large contextual dataset for classification detection and counting of cars with deep learning"
    - "ssd single shot multibox detector"
    - "speed accuracy trade offs for modern convolutional object detectors"
    - "deep residual learning for image recognition"
    - "fast r cnn"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-8 (induced by source paper "class agnostic counting"):
  with members:
    - "composition loss for counting density map estimation and localization in dense crowds"
    - "iterative crowd counting"
    - "learning to compare relation network for few shot learning"
    - "drone based object counting by spatially regularized regional proposal network"
    - "a large contextual dataset for classification detection and counting of cars with deep learning"
    - "fully convolutional siamese networks for object tracking"
    - "matching networks for one shot learning"
    - "you only look once unified real time object 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)