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: "learning deep convolutional neural networks for places2 scene recognition"
  Abstract: We describe the optimization algorithm and methodologies we used in the ILSVRC 2015 Scene Classification Challenge, which we won the first place.

Connected through hyperedge HE-1 (induced by source paper "weakly supervised patchnets describing and aggregating local patches for scene recognition"):
  with members:
    - "hybrid cnn and dictionary based models for scene recognition and domain adaptation"
    - "knowledge guided disambiguation for large scale scene classification with multi resolution cnns"
    - "locally supervised deep hybrid model for scene recognition"
    - "transferring object scene convolutional neural networks for event recognition in still images"
    - "learning contextual dependence with convolutional hierarchical recurrent neural networks"
    - "actionness estimation using hybrid fully convolutional networks"
    - "temporal segment networks towards good practices for deep action recognition"
    - "object scene convolutional neural networks for event recognition in images"

Connected through hyperedge HE-2 (induced by source paper "e 2 bows an end to end bag of words model via deep convolutional neural network"):
  with members:
    - "deep semantic ranking based hashing for multi label image retrieval"
    - "simultaneous feature learning and hash coding with deep neural networks"
    - "deep residual learning for image recognition"
    - "facenet a unified embedding for face recognition and clustering"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "fully convolutional networks for semantic segmentation"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

Connected through hyperedge HE-3 (induced by source paper "deeply fused nets"):
  with members:
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "all you need is a good init"
    - "holistically nested edge detection"
    - "batch normalized maxout network in network"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "net2net accelerating learning via knowledge transfer"
    - "competitive multi scale convolution"

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)