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: "personalized social recommendations accurate or private"
  Abstract: With the recent surge of social networks like Facebook, new forms of recommendations have become possible - personalized recommendations of ads, content, and even new friend and product connections based on one's social interactions. Since recommendations may use sensitive social information, it is speculated that these recommendations are associated with privacy risks. The main contribution of this work is in formalizing these expected trade-offs between the accuracy and privacy of personalized social recommendations. #R##N#In this paper, we study whether "social recommendations", or recommendations that are solely based on a user's social network, can be made without disclosing sensitive links in the social graph. More precisely, we quantify the loss in utility when existing recommendation algorithms are modified to satisfy a strong notion of privacy, called differential privacy. We prove lower bounds on the minimum loss in utility for any recommendation algorithm that is differentially private. We adapt two privacy preserving algorithms from the differential privacy literature to the problem of social recommendations, and analyze their performance in comparison to the lower bounds, both analytically and experimentally. We show that good private social recommendations are feasible only for a small subset of the users in the social network or for a lenient setting of privacy parameters.

Connected through hyperedge HE-1 (induced by source paper "prince provider side interpretability with counterfactual explanations in recommender systems"):
  with members:
    - "jointly learning explainable rules for recommendation with knowledge graph"
    - "fairy a framework for understanding relationships between users actions and their social feeds"
    - "reinforcement knowledge graph reasoning for explainable recommendation"
    - "learning heterogeneous knowledge base embeddings for explainable recommendation"
    - "ripplenet propagating user preferences on the knowledge graph for recommender systems"
    - "explainable reasoning over knowledge graphs for recommendation"
    - "explainable recommendation via multi task learning in opinionated text data"
    - "explainable recommendation a survey and new perspectives"

Connected through hyperedge HE-2 (induced by source paper "privacy preserving link prediction with latent geometric network models"):
  with members:
    - "link prediction based on graph neural networks"
    - "struc2vec learning node representations from structural identity"
    - "node2vec scalable feature learning for networks"
    - "line large scale information network embedding"
    - "fast differentially private matrix factorization"
    - "discriminative link prediction using local links node features and community structure"
    - "mining frequent graph patterns with differential privacy"
    - "calibrating data to sensitivity in private data analysis"

Connected through hyperedge HE-3 (induced by source paper "when and where do you want to hide recommendation of location privacy preferences with local differential privacy"):
  with members:
    - "quantifying differential privacy in continuous data release under temporal correlations"
    - "priste from location privacy to spatiotemporal event privacy"
    - "quantifying differential privacy under temporal correlations"
    - "fast differentially private matrix factorization"
    - "geo indistinguishability differential privacy for location based systems"

Connected through hyperedge HE-4 (induced by source paper "privacy in social media identification mitigation and applications"):
  with members:
    - "social media and user privacy"
    - "similar but different exploiting users congruity for recommendation systems"
    - "a survey of location prediction on twitter"
    - "securing social media user data an adversarial approach"
    - "blind de anonymization attacks using social networks"
    - "walk2friends inferring social links from mobility profiles"
    - "on content based recommendation and user privacy in social tagging systems"
    - "identifying community structures in dynamic networks"

Connected through hyperedge HE-5 (induced by source paper "syntf synthetic and differentially private term frequency vectors for privacy preserving text mining"):
  with members:
    - "rappor randomized aggregatable privacy preserving ordinal response"
    - "blowfish privacy tuning privacy utility trade offs using policies"
    - "geo indistinguishability differential privacy for location based systems"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"
    - "calibrating data to sensitivity in private data analysis"
    - "the numpy array a structure for efficient numerical computation"
    - "message classification in the call center"

Connected through hyperedge HE-6 (induced by source paper "protecting user privacy an approach for untraceable web browsing history and unambiguous user profiles"):
  with members:
    - "privacy in social media identification mitigation and applications"
    - "similar but different exploiting users congruity for recommendation systems"
    - "early identification of pathogenic social media accounts"
    - "securing social media user data an adversarial approach"
    - "semi supervised learning for detecting human trafficking"
    - "a utility theoretic approach to privacy in online services"
    - "de anonymizing social networks"

Connected through hyperedge HE-7 (induced by source paper "towards a more reliable privacy preserving recommender system"):
  with members:
    - "fast differentially private matrix factorization"
    - "rappor randomized aggregatable privacy preserving ordinal response"
    - "bpr bayesian personalized ranking from implicit feedback"
    - "calibrating data to sensitivity in private data analysis"

Connected through hyperedge HE-8 (induced by source paper "differentially private regression for discrete time survival analysis"):
  with members:
    - "bayesian posterior sampling via stochastic gradient fisher scoring"
    - "differentially private empirical risk minimization"

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.DB(Databases)