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 "building graph representations of deep vector embeddings"):
  Abstract: Patterns stored within pre-trained deep neural networks compose large and powerful descriptive languages that can be used for many different purposes. Typically, deep network representations are implemented within vector embedding spaces, which enables the use of traditional machine learning algorithms on top of them. In this short paper we propose the construction of a graph embedding space instead, introducing a methodology to transform the knowledge coded within a deep convolutional network into a topological space (i.e. a network). We outline how such graph can hold data instances, data features, relations between instances and features, and relations among features. Finally, we introduce some preliminary experiments to illustrate how the resultant graph embedding space can be exploited through graph analytics algorithms.

Includes members:
  Member "an out of the box full network embedding for convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "full network embedding in a multimodal embedding pipeline")
  Member "fluid communities a competitive scalable and diverse community detection algorithm":
    also connected through other hyperedges:
      (none)
  Member "on the behavior of convolutional nets for feature extraction":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "classification of crop tolerance to heat and drought a deep convolutional neural networks approach")
  Member "borrowing treasures from the wealthy deep transfer learning through selective joint fine tuning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "rethinking the hyperparameters for fine tuning")
  Member "deepfood deep learning based food image recognition for computer aided dietary assessment":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multi task self supervised learning for human activity detection")
  Member "neural activation constellations unsupervised part model discovery with convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "interpretable cnns for object classification")
  Member "deep convolutional features for image based retrieval and scene categorization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "mildnet a lightweight single scaled deep ranking architecture")
  Member "deepwalk online learning of social representations":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "memory based graph networks")

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.NE(Neural and Evolutionary Computing)