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: "a survey of point of interest recommendation in location based social networks"
  Abstract: Point-of-interest (POI) recommendation that suggests new places for users to visit arises with the popularity of location-based social networks (LBSNs). Due to the importance of POI recommendation in LBSNs, it has attracted much academic and industrial interest. In this paper, we offer a systematic review of this field, summarizing the contributions of individual efforts and exploring their relations. We discuss the new properties and challenges in POI recommendation, compared with traditional recommendation problems, e.g., movie recommendation. Then, we present a comprehensive review in three aspects: influential factors for POI recommendation, methodologies employed for POI recommendation, and different tasks in POI recommendation. Specifically, we propose three taxonomies to classify POI recommendation systems. First, we categorize the systems by the influential factors check-in characteristics, including the geographical information, social relationship, temporal influence, and content indications. Second, we categorize the systems by the methodology, including systems modeled by fused methods and joint methods. Third, we categorize the systems as general POI recommendation and successive POI recommendation by subtle differences in the recommendation task whether to be bias to the recent check-in. For each category, we summarize the contributions and system features, and highlight the representative work. Moreover, we discuss the available data sets and the popular metrics. Finally, we point out the possible future directions in this area and conclude this survey.

Connected through hyperedge HE-1 (induced by source paper "practical privacy preserving poi recommendation"):
  with members:
    - "semi supervised learning meets factorization learning to recommend with chain graph model"
    - "privacy preserving point of interest recommendation using decentralized matrix factorization"
    - "secure social recommendation based on secret sharing"
    - "federated learning strategies for improving communication efficiency"
    - "billion scale commodity embedding for e commerce recommendation in alibaba"
    - "distributed collaborative hashing and its applications in ant financial"
    - "neural collaborative filtering"
    - "deepfm a factorization machine based neural network for ctr prediction"

Connected through hyperedge HE-2 (induced by source paper "detect deep trajectory clustering for mobility behavior analysis"):
  with members:
    - "autowarp learning a warping distance from unlabeled time series using sequence autoencoders"
    - "a critical review of recurrent neural networks for sequence learning"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "unsupervised deep embedding for clustering analysis"
    - "semi supervised sequence learning"
    - "vector field k means clustering trajectories by fitting multiple vector fields"

Connected through hyperedge HE-3 (induced by source paper "leveraging prior knowledge asymmetries in the design of location privacy preserving mechanisms"):
  with members:
    - "privacy of dependent users against statistical matching"
    - "matching anonymized and obfuscated time series to users profiles"
    - "is geo indistinguishability what you are looking for"
    - "optimal geo indistinguishable mechanisms for location privacy"

Connected through hyperedge HE-4 (induced by source paper "a survey on the security of pervasive online social networks posns"):
  with members:
    - "a framework for mitigating zero day attacks in iot"
    - "nhad neuro fuzzy based horizontal anomaly detection in online social networks"
    - "self enforcing game theory based resource allocation for lorawan assisted public safety communications"
    - "managing service heterogeneity using osmotic computing"
    - "netspam a network based spam detection framework for reviews in online social media"
    - "a privacy preserving community based p2p osns using broadcast encryption supporting recommendation mechanism"
    - "extension of mih to support fpmipv6 for optimized heterogeneous handover"
    - "analyzing resilience of interest based social networks against node and link failures"

Connected through hyperedge HE-5 (induced by source paper "a survey of location prediction on twitter"):
  with members:
    - "continuous representation of location for geolocation and lexical dialectology using mixture density networks"
    - "a neural model for user geolocation and lexical dialectology"
    - "density estimation for geolocation via convolutional mixture density network"
    - "exploiting text and network context for geolocation of social media users"
    - "analysis of named entity recognition and linking for tweets"
    - "twitter user geolocation using a unified text and network prediction model"
    - "a survey of location inference techniques on twitter"
    - "geo located twitter as proxy for global mobility patterns"

Connected through hyperedge HE-6 (induced by source paper "personalized next point of interest recommendation via latent behavior patterns inference"):
  with members:
    - "vbpr visual bayesian personalized ranking from implicit feedback"
    - "bpr bayesian personalized ranking from implicit feedback"

Connected through hyperedge HE-7 (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"
    - "geo sage a geographical sparse additive generative model for spatial item recommendation"

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)