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 "event ticket price prediction with deep neural network on spatial temporal sparse data"):
  Abstract: Event ticket price prediction is important to marketing strategy for any sports team or musical ensemble. An accurate prediction model can help the marketing team to make promotion plan more effectively and efficiently. However, given all the historical transaction records, it is challenging to predict the sale price of the remaining seats at any future timestamp, not only because that the sale price is relevant to a lot of features (seat locations, date-to-event of the transaction, event date, team performance, etc.), but also because of the temporal and spatial sparsity in the dataset. For a game/concert, the ticket selling price of one seat is only observable once at the time of sale. Furthermore, some seats may not even be purchased (therefore no record available). In fact, data sparsity is commonly encountered in many prediction problems. Here, we propose a bi-level optimizing deep neural network to address the curse of spatio-temporal sparsity. Specifically, we introduce coarsening and refining layers, and design a bi-level loss function to integrate different level of loss for better prediction accuracy. Our model can discover the interrelations among ticket sale price, seat locations, selling time, event information, etc. Experiments show that our proposed model outperforms other benchmark methods in real-world ticket selling price prediction.

Includes members:
  Member "revisiting spatial temporal similarity a deep learning framework for traffic prediction":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "demand forecasting from spatiotemporal data with graph networks and temporal guided embedding")
  Member "spatio temporal graph convolutional networks a deep learning framework for traffic forecasting":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "physical virtual collaboration graph network for station level metro ridership prediction")
  Member "deep multi view spatial temporal network for taxi demand prediction":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "revisiting convolutional neural networks for urban flow analytics")
  Member "short term forecasting of passenger demand under on demand ride services a spatio temporal deep learning approach":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "interpretable crowd flow prediction with spatial temporal self attention")
  Member "spatiotemporal recurrent convolutional networks for traffic prediction in transportation networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a neural architecture for detecting confusion in eye tracking data")
  Member "machine learning modeling for time series problem predicting flight ticket prices":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "compnet neural networks growing via the compact network morphism")
  Member "xgboost a scalable tree boosting system":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "monotonic cardinality estimation of similarity selection a deep learning approach")
  Member "deep spatio temporal residual networks for citywide crowd flows prediction":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "disentangling physical dynamics from unknown factors for unsupervised video prediction")

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.LG(Machine Learning)