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: "c 3 framework an open source pytorch code for crowd counting"
  Abstract: This technical report attempts to provide efficient and solid kits addressed on the field of crowd counting, which is denoted as Crowd Counting Code Framework (C$^3$F). The contributions of C$^3$F are in three folds: 1) Some solid baseline networks are presented, which have achieved the state-of-the-arts. 2) Some flexible parameter setting strategies are provided to further promote the performance. 3) A powerful log system is developed to record the experiment process, which can enhance the reproducibility of each experiment. Our code is made publicly available at \url{this https URL}. Furthermore, we also post a Chinese blog\footnote{\url{this https URL}} to describe the details and insights of crowd counting.

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"
    - "the apolloscape open dataset for autonomous driving and its application"
    - "perspective guided convolution networks for crowd counting"
    - "pushing the frontiers of unconstrained crowd counting new dataset and benchmark method"

Connected through hyperedge HE-2 (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"
    - "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"
    - "crowd counting using deep recurrent spatial aware network"

Connected through hyperedge HE-3 (induced by source paper "focus on semantic consistency for cross domain crowd understanding"):
  with members:
    - "scar spatial channel wise attention regression networks for crowd counting"
    - "sliced wasserstein discrepancy for unsupervised domain adaptation"
    - "transferrable prototypical networks for unsupervised domain adaptation"
    - "learning from synthetic data for crowd counting in the wild"
    - "pcc net perspective crowd counting via spatial convolutional network"
    - "composition loss for counting density map estimation and localization in dense crowds"
    - "learning to adapt structured output space for semantic segmentation"
    - "adaptive scenario discovery for crowd counting"

Connected through hyperedge HE-4 (induced by source paper "crowd density forecasting by modeling patch based dynamics"):
  with members:
    - "locality constrained spatial transformer network for video crowd counting"
    - "sr lstm state refinement for lstm towards pedestrian trajectory prediction"
    - "human motion trajectory prediction a survey"
    - "looking to relations for future trajectory forecast"
    - "multi agent tensor fusion for contextual trajectory prediction"
    - "discrete residual flow for probabilistic pedestrian behavior prediction"
    - "precog prediction conditioned on goals in visual multi agent settings"
    - "future near collision prediction from monocular video feasibility dataset and challenges"

Connected through hyperedge HE-5 (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"
    - "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"
    - "composition loss for counting density map estimation and localization in dense crowds"

Connected through hyperedge HE-6 (induced by source paper "domain adaptive crowd counting via inter domain features segregation and gaussian prior reconstruction"):
  with members:
    - "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"
    - "pushing the frontiers of unconstrained crowd counting new dataset and benchmark method"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "scar spatial channel wise attention regression networks for crowd counting"):
  with members:
    - "hierarchical recurrent neural network for video summarization"
    - "weakly supervised adversarial domain adaptation for semantic segmentation in urban scenes"
    - "learning from synthetic data for crowd counting in the wild"
    - "pcc net perspective crowd counting via spatial convolutional network"
    - "composition loss for counting density map estimation and localization in dense crowds"
    - "crowd counting using deep recurrent spatial aware network"
    - "exploring models and data for remote sensing image caption generation"
    - "csrnet dilated convolutional neural networks for understanding the highly congested scenes"

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)