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 "learning event representation as sparse as possible but not sparser"):
  Abstract: Selecting an optimal event representation is essential for event classification in real world contexts. In this paper, we investigate the application of qualitative spatial reasoning (QSR) frameworks for classification of human-object interaction in three dimensional space, in comparison with the use of quantitative feature extraction approaches for the same purpose. In particular, we modify QSRLib, a library that allows computation of Qualitative Spatial Relations and Calculi, and employ it for feature extraction, before inputting features into our neural network models. Using an experimental setup involving motion captures of human-object interaction as three dimensional inputs, we observe that the use of qualitative spatial features significantly improves the performance of our machine learning algorithm against our baseline, while quantitative features of similar kinds fail to deliver similar improvement. We also observe that sequential representations of QSR features yield the best classification performance. A result of our learning method is a simple approach to the qualitative representation of 3D activities as compositions of 2D actions that can be visualized and learned using 2-dimensional QSR.

Includes members:
  Member "fine grained event learning of human object interaction with lstm crf":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multimodal semantic simulations of linguistically underspecified motion events")
  Member "ntu rgb d a large scale dataset for 3d human activity analysis":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a survey on 3d skeleton based action recognition using learning method")
  Member "ecat event capture annotation tool":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multimodal interactive learning of primitive actions")
  Member "deep learning in neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "π nets deep polynomial neural networks")
  Member "bidirectional lstm crf models for sequence tagging":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "dense dilated convolutions merging network for land cover classification")
  Member "qtc3d extending the qualitative trajectory calculus to three dimensions":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "towards explainable inference about object motion using qualitative reasoning")
  Member "improving neural networks by preventing co adaptation of feature detectors":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "hyper parameter optimization a review of algorithms and applications")

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.CV(Computer Vision and Pattern Recognition)