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: "relating cascaded random forests to deep convolutional neural networks for semantic segmentation"
  Abstract: We consider the task of pixel-wise semantic segmentation given a small set of labeled training images. Among two of the most popular techniques to address this task are Random Forests (RF) and Neural Networks (NN). The main contribution of this work is to explore the relationship between two special forms of these techniques: stacked RFs and deep Convolutional Neural Networks (CNN). We show that there exists a mapping from stacked RF to deep CNN, and an approximate mapping back. This insight gives two major practical benefits: Firstly, deep CNNs can be intelligently constructed and initialized, which is crucial when dealing with a limited amount of training data. Secondly, it can be utilized to create a new stacked RF with improved performance. Furthermore, this mapping yields a new CNN architecture, that is well suited for pixel-wise semantic labeling. We experimentally verify these practical benefits for two different application scenarios in computer vision and biology, where the layout of parts is important: Kinect-based body part labeling from depth images, and somite segmentation in microscopy images of developing zebrafish.

Connected through hyperedge HE-1 (induced by source paper "dynamic connected neural decision classifier and regressor with dynamic softing pruning"):
  with members:
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "dynamic network surgery for efficient dnns"
    - "deep residual learning for image recognition"
    - "openml networked science in machine learning"

Connected through hyperedge HE-2 (induced by source paper "semantic video segmentation a review on recent approaches"):
  with members:
    - "stfcn spatio temporal fcn for semantic video segmentation"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "std2p rgbd semantic segmentation using spatio temporal data driven pooling"
    - "optical flow with semantic segmentation and localized layers"
    - "convolutional random walk networks for semantic image segmentation"
    - "semantic image segmentation via deep parsing network"
    - "deep hierarchical parsing for semantic segmentation"
    - "attention to scale scale aware semantic image segmentation"

Connected through hyperedge HE-3 (induced by source paper "deep forest"):
  with members:
    - "distributed deep forest and its application to automatic detection of cash out fraud"
    - "thinet a filter level pruning method for deep neural network compression"
    - "classification under streaming emerging new classes a solution using completely random trees"
    - "very deep convolutional networks for large scale image recognition"
    - "convolutional neural networks for sentence classification"
    - "solving multiclass learning problems via error correcting output codes"

Connected through hyperedge HE-4 (induced by source paper "enhancing multi class classification of random forest using random vector functional neural network and oblique decision surfaces"):
  with members:
    - "a system for induction of oblique decision trees"

Connected through hyperedge HE-5 (induced by source paper "object recognition from very few training examples for enhancing bicycle maps"):
  with members:
    - "deep learning for vanishing point detection using an inverse gnomonic projection"
    - "on support relations and semantic scene graphs"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "ssd single shot multibox detector"
    - "yolo9000 better faster stronger"
    - "random forests versus neural networks what s best for camera localization"
    - "learning to detect and localize many objects from few examples"
    - "fast r cnn"

Connected through hyperedge HE-6 (induced by source paper "ndt neual decision tree towards fully functioned neural graph"):
  with members:
    - "hungarian layer logics empowered neural architecture"
    - "deep forest towards an alternative to deep neural networks"
    - "all you need is a good init"
    - "deep residual learning for image recognition"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "training very deep networks"
    - "striving for simplicity the all convolutional net"
    - "network in network"

Connected through hyperedge HE-7 (induced by source paper "deep embedding forest forest based serving with deep embedding features"):
  with members:
    - "deep learning on fpgas past present and future"
    - "xgboost a scalable tree boosting system"
    - "a communication efficient parallel algorithm for decision tree"
    - "deep residual learning for image recognition"
    - "cudnn efficient primitives for deep learning"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-8 (induced by source paper "stfcn spatio temporal fcn for semantic video segmentation"):
  with members:
    - "std2p rgbd semantic segmentation using spatio temporal data driven pooling"
    - "deep hierarchical parsing for semantic segmentation"
    - "flownet learning optical flow with convolutional networks"
    - "attention to scale scale aware semantic image segmentation"
    - "multi scale context aggregation by dilated convolutions"
    - "segdeepm exploiting segmentation and context in deep neural networks for object detection"
    - "grid long short term memory"
    - "gated feedback recurrent neural networks"

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)