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: "pcc net perspective crowd counting via spatial convolutional network"
  Abstract: Crowd counting from a single image is a challenging task due to high appearance similarity, perspective changes and severe congestion. Many methods only focus on the local appearance features and they cannot handle the aforementioned challenges. In order to tackle them, we propose a Perspective Crowd Counting Network (PCC Net), which consists of three parts: 1) Density Map Estimation (DME) focuses on learning very local features for density map estimation; 2) Random High-level Density Classification (R-HDC) extracts global features to predict the coarse density labels of random patches in images; 3) Fore-/Background Segmentation (FBS) encodes mid-level features to segments the foreground and background. Besides, the DULR module is embedded in PCC Net to encode the perspective changes on four directions (Down, Up, Left and Right). The proposed PCC Net is verified on five mainstream datasets, which achieves the state-of-the-art performance on the one and attains the competitive results on the other four datasets. The source code is available at this https URL.

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 "focus on semantic consistency for cross domain crowd understanding"):
  with members:
    - "scar spatial channel wise attention regression networks for crowd counting"
    - "c 3 framework an open source pytorch code 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"
    - "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-3 (induced by source paper "fast video crowd counting with a temporal aware network"):
  with members:
    - "scar spatial channel wise attention regression networks for crowd counting"
    - "learning spatial awareness to improve crowd counting"
    - "ms tcn multi stage temporal convolutional network for action segmentation"
    - "learning from synthetic data for crowd counting in the wild"
    - "attend to count crowd counting with adaptive capacity multi scale cnns"
    - "crowd counting with deep structured scale integration network"
    - "composition loss for counting density map estimation and localization in dense crowds"
    - "adaptive scenario discovery for crowd counting"

Connected through hyperedge HE-4 (induced by source paper "highlight every step knowledge distillation via collaborative teaching"):
  with members:
    - "towards optimal structured cnn pruning via generative adversarial learning"
    - "model compression via distillation and quantization"
    - "private model compression via knowledge distillation"
    - "amalgamating knowledge towards comprehensive classification"
    - "improving fast segmentation with teacher student learning"
    - "label refinery improving imagenet classification through label progression"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "progressive neural architecture search"

Connected through hyperedge HE-5 (induced by source paper "crowd counting with deep structured scale integration network"):
  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"
    - "context aware crowd counting"
    - "csrnet dilated convolutional neural networks for understanding the highly congested scenes"
    - "iterative crowd counting"
    - "crowd counting by adaptively fusing predictions from an image pyramid"
    - "attentive crowd flow machines"
    - "adcrowdnet an attention injective deformable convolutional network for crowd understanding"

Connected through hyperedge HE-6 (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"
    - "multi level bottom top and top bottom feature fusion for crowd counting"
    - "dynamic region division for adaptive learning pedestrian counting"
    - "learning from synthetic data for crowd counting in the wild"
    - "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-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"
    - "c 3 framework an open source pytorch code for crowd counting"
    - "learning from synthetic data for crowd counting in the wild"
    - "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)