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 "unsupervised spatio temporal embeddings for user and location modelling"):
  Abstract: Many social network applications depend on robust representations of spatio-temporal data. In this work, we present an embedding model based on feed-forward neural networks which transforms social media check-ins into dense feature vectors encoding geographic, temporal, and functional aspects for modelling places, neighborhoods, and users. On the basis of this model, we further propose a Spatio-Temporal Embedding Similarity algorithm (STES) for location recommendation. #R##N#In a range of experiments on real life data collected from Foursquare, Twitter, and Gowalla, we demonstrate our model's effectiveness at characterizing places and people, resulting in a significantly increased recommendation and classification performance as compared to the state of the art. Finally, we select eight major cities around the globe and verify the robustness and generality of our model by porting pre-trained models from one city to another, and thereby alleviating the need for costly local training.

Includes members:
  Member "a structured self attentive sentence embedding":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "high order paired aspp networks for semantic segmenation")
  Member "word embeddings to enhance twitter gang member profile identification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "predictive analysis on twitter techniques and applications")
  Member "semantic place descriptors for classification and map discovery":
    also connected through other hyperedges:
      (none)
  Member "from word embeddings to item recommendation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a survey on session based recommender systems")
  Member "gt seer geo temporal sequential embedding rank for point of interest recommendation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "mpe a mobility pattern embedding model for predicting next locations")
  Member "deep residual learning for image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "geo sage a geographical sparse additive generative model for spatial item recommendation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "relation embedding for personalised poi recommendation")
  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.IR(Information Retrieval)