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: "deep learning for real time crime forecasting and its ternarization"
  Abstract: Real-time crime forecasting is important. However, accurate prediction of when and where the next crime will happen is difficult. No known physical model provides a reasonable approximation to such a complex system. Historical crime data are sparse in both space and time and the signal of interests is weak. In this work, we first present a proper representation of crime data. We then adapt the spatial temporal residual network on the well represented data to predict the distribution of crime in Los Angeles at the scale of hours in neighborhood-sized parcels. These experiments as well as comparisons with several existing approaches to prediction demonstrate the superiority of the proposed model in terms of accuracy. Finally, we present a ternarization technique to address the resource consumption issue for its deployment in real world. This work is an extension of our short conference proceeding paper [Wang et al, Arxiv 1707.03340].

Connected through hyperedge HE-1 (induced by source paper "a comparative study on crime in denver city based on machine learning and data mining"):
  with members:
    - "crime mapping from satellite imagery via deep learning"
    - "crime prediction based on crime types and using spatial and temporal criminal hotspots"

Connected through hyperedge HE-2 (induced by source paper "a study on graph structured recurrent neural networks and sparsification with application to epidemic forecasting"):
  with members:
    - "graph based deep modeling and real time forecasting of sparse spatio temporal data"
    - "laplacian smoothing gradient descent"
    - "structural rnn deep learning on spatio temporal graphs"
    - "minimization of transformed l_1 penalty theory difference of convex function algorithm and robust application in compressed sensing"
    - "on the difficulty of training recurrent neural networks"

Connected through hyperedge HE-3 (induced by source paper "where are we using scopus to map the literature at the intersection between artificial intelligence and crime"):
  with members:
    - "arming the public with artificial intelligence to counter social bots"
    - "transfer learning from deep features for remote sensing and poverty mapping"
    - "prediction of emerging technologies based on analysis of the us patent citation network"
    - "mapping excellence in the geography of science an approach based on scopus data"

Connected through hyperedge HE-4 (induced by source paper "graph based deep modeling and real time forecasting of sparse spatio temporal data"):
  with members:
    - "gated graph sequence neural networks"
    - "deep residual learning for image recognition"
    - "structural rnn deep learning on spatio temporal graphs"
    - "adam a method for stochastic optimization"

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.LG(Machine Learning)