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: "geo sage a geographical sparse additive generative model for spatial item recommendation"
  Abstract: With the rapid development of location-based social networks (LBSNs), spatial item recommendation has become an important means to help people discover attractive and interesting venues and events, especially when users travel out of town. However, this recommendation is very challenging compared to the traditional recommender systems. A user can visit only a limited number of spatial items, leading to a very sparse user-item matrix. Most of the items visited by a user are located within a short distance from where he/she lives, which makes it hard to recommend items when the user travels to a far away place. Moreover, user interests and behavior patterns may vary dramatically across different geographical regions. In light of this, we propose Geo-SAGE, a geographical sparse additive generative model for spatial item recommendation in this paper. Geo-SAGE considers both user personal interests and the preference of the crowd in the target region, by exploiting both the co-occurrence pattern of spatial items and the content of spatial items. To further alleviate the data sparsity issue, Geo-SAGE exploits the geographical correlation by smoothing the crowd's preferences over a well-designed spatial index structure called spatial pyramid. We conduct extensive experiments to evaluate the performance of our Geo-SAGE model on two real large-scale datasets. The experimental results clearly demonstrate our Geo-SAGE model outperforms the state-of-the-art in the two tasks of both out-of-town and home-town recommendations.

Connected through hyperedge HE-1 (induced by source paper "relation embedding for personalised poi recommendation"):
  with members:
    - "fisher bures adversary graph convolutional networks"
    - "translation based recommendation"
    - "heterogeneous information network embedding for recommendation"
    - "a comprehensive survey of graph embedding problems techniques and applications"
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"
    - "modeling relation paths for representation learning of knowledge bases"
    - "deepwalk online learning of social representations"

Connected through hyperedge HE-2 (induced by source paper "exploring student check in behavior for improved point of interest prediction"):
  with members:
    - "improving negative sampling for word representation using self embedded features"
    - "task guided and path augmented heterogeneous network embedding for author identification"
    - "pte predictive text embedding through large scale heterogeneous text networks"
    - "line large scale information network embedding"
    - "deepwalk online learning of social representations"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-3 (induced by source paper "unsupervised spatio temporal embeddings for user and location modelling"):
  with members:
    - "a structured self attentive sentence embedding"
    - "word embeddings to enhance twitter gang member profile identification"
    - "semantic place descriptors for classification and map discovery"
    - "from word embeddings to item recommendation"
    - "gt seer geo temporal sequential embedding rank for point of interest recommendation"
    - "deep residual learning for image recognition"
    - "deepwalk online learning of social representations"
    - "exploiting similarities among languages for machine translation"

Connected through hyperedge HE-4 (induced by source paper "leveraging multi aspect time related influence in location recommendation"):
  with members:
    - "gt seer geo temporal sequential embedding rank for point of interest recommendation"
    - "a survey of point of interest recommendation in location based social networks"

Connected through hyperedge HE-5 (induced by source paper "extracting patterns of urban activity from geotagged social data"):
  with members:
    - "on the accuracy of hyper local geotagging of social media content"
    - "hoodsquare modeling and recommending neighborhoods in location based social networks"

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.IR(Information Retrieval)