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: "hierarchical scene parsing by weakly supervised learning with image descriptions"
  Abstract: This paper investigates a fundamental problem of scene understanding: how to parse a scene image into a structured configuration (i.e., a semantic object hierarchy with object interaction relations). We propose a deep architecture consisting of two networks: i) a convolutional neural network (CNN) extracting the image representation for pixel-wise object labeling and ii) a recursive neural network (RsNN) discovering the hierarchical object structure and the inter-object relations. Rather than relying on elaborative annotations (e.g., manually labeled semantic maps and relations), we train our deep model in a weakly-supervised learning manner by leveraging the descriptive sentences of the training images. Specifically, we decompose each sentence into a semantic tree consisting of nouns and verb phrases, and apply these tree structures to discover the configurations of the training images. Once these scene configurations are determined, then the parameters of both the CNN and RsNN are updated accordingly by back propagation. The entire model training is accomplished through an Expectation-Maximization method. Extensive experiments show that our model is capable of producing meaningful scene configurations and achieving more favorable scene labeling results on two benchmarks (i.e., PASCAL VOC 2012 and SYSU-Scenes) compared with other state-of-the-art weakly-supervised deep learning methods. In particular, SYSU-Scenes contains more than 5000 scene images with their semantic sentence descriptions, which is created by us for advancing research on scene parsing.

Connected through hyperedge HE-1 (induced by source paper "attentive crowd flow machines"):
  with members:
    - "crowd counting using deep recurrent spatial aware network"
    - "cross city transfer learning for deep spatio temporal prediction"
    - "deep multi view spatial temporal network for taxi demand prediction"
    - "spatiotemporal modeling for crowd counting in videos"
    - "multi label image recognition by recurrently discovering attentional regions"
    - "attention aware face hallucination via deep reinforcement learning"
    - "fcn rlstm deep spatio temporal neural networks for vehicle counting in city cameras"
    - "deeptrend a deep hierarchical neural network for traffic flow prediction"

Connected through hyperedge HE-2 (induced by source paper "weakly supervised scene parsing with point based distance metric learning"):
  with members:
    - "adversarial complementary learning for weakly supervised object localization"
    - "revisiting dilated convolution a simple approach for weakly and semi supervised semantic segmentation"
    - "self erasing network for integral object attention"
    - "recurrent pixel embedding for instance grouping"
    - "object region mining with adversarial erasing a simple classification to semantic segmentation approach"
    - "stc a simple to complex framework for weakly supervised semantic segmentation"
    - "semantic instance segmentation via deep metric learning"
    - "seed expand and constrain three principles for weakly supervised image segmentation"

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)