human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "arctext an unified text approach to describing convolutional neural network architectures"):
  Abstract: Numerous Convolutional Neural Network (CNN) models have demonstrated their promising performance mostly in computer vision. The superiority of CNNs mainly relies on their complex architectures that are often manually designed with extensive human expertise. Data mining on CNN architectures can discover useful patterns and fundamental sub-comments from existing CNN architectures, providing common researchers with strong prior knowledge to design CNN architectures when they have no expertise in CNNs. There have been various state-of-the-art data mining algorithms at hand, while there is rare work that has been used for this aspect. The main reason behind this is the barrier between CNN architectures and data mining algorithms. Specifically, the current CNN architecture descriptions cannot be exactly vectorized to the input to data mining algorithms. In this paper, we propose a unified approach, named ArcTxt, to describing CNN architectures based on text. Particularly, three different units of ArcText and an order method have been elaborately designed, to uniquely describe the same architecture including the sufficient information. Also, the resulted description can also be exactly converted back to the corresponding CNN architecture. ArcText bridge the gap between CNN and data mining researchers, and has the potentiality to be utilized to wider scenarios.

Includes members:
  Member "automatically designing cnn architectures using genetic algorithm for image classification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "pairnets novel fast shallow artificial neural networks on partitioned subspaces")
  Member "peephole predicting network performance before training":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "econas finding proxies for economical neural architecture search")
  Member "deep networks with stochastic depth":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on feature normalization and data augmentation")
  Member "identity mappings in deep residual networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "overfitting in adversarially robust deep learning")
  Member "neural architecture search with reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "deep residual learning for image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "going deeper with convolutions":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "discrimination aware network pruning for deep model compression")

Question: Please tell me which class the source paper 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.IR(Information Retrieval)