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: "latent collaborative retrieval"
  Abstract: Retrieval tasks typically require a ranking of items given a query. Collaborative filtering tasks, on the other hand, learn to model user's preferences over items. In this paper we study the joint problem of recommending items to a user with respect to a given query, which is a surprisingly common task. This setup differs from the standard collaborative filtering one in that we are given a query x user x item tensor for training instead of the more traditional user x item matrix. Compared to document retrieval we do have a query, but we may or may not have content features (we will consider both cases) and we can also take account of the user's profile. We introduce a factorized model for this new task that optimizes the top-ranked items returned for the given query and user. We report empirical results where it outperforms several baselines.

Connected through hyperedge HE-1 (induced by source paper "core automating review recommendation for code changes"):
  with members:
    - "an empirical assessment of security risks of global android banking apps"
    - "pythia ai assisted code completion system"
    - "an empirical study towards characterizing deep learning development and deployment across different frameworks and platforms"
    - "storydroid automated generation of storyboard for android apps"
    - "ausera large scale automated security risk assessment of global mobile banking apps"
    - "efficiently manifesting asynchronous programming errors in android apps"
    - "code completion with neural attention and pointer networks"
    - "deep contextualized word representations"

Connected through hyperedge HE-2 (induced by source paper "attribute aware collaborative filtering survey and classification"):
  with members:
    - "atrank an attention based user behavior modeling framework for recommendation"
    - "neural factorization machines for sparse predictive analytics"
    - "neural collaborative filtering"
    - "on sampling strategies for neural network based collaborative filtering"
    - "deepfm a factorization machine based neural network for ctr prediction"
    - "low rank tensor completion a riemannian manifold preconditioning approach"
    - "collaborative recurrent autoencoder recommend while learning to fill in the blanks"
    - "multi view machines"

Connected through hyperedge HE-3 (induced by source paper "vse ens visual semantic embeddings with efficient negative sampling"):
  with members:
    - "automatic image annotation via label transfer in the semantic space"
    - "multi instance visual semantic embedding"
    - "social collaborative retrieval"
    - "unifying visual semantic embeddings with multimodal neural language models"
    - "bpr bayesian personalized ranking from implicit feedback"
    - "on the consistency of auc pairwise optimization"

Connected through hyperedge HE-4 (induced by source paper "non linear label ranking for large scale prediction of long term user interests"):
  with members:
    - "know your personalization learning topic level personalization in online services"

Connected through hyperedge HE-5 (induced by source paper "a survey of point of interest recommendation in location based social networks"):
  with members:
    - "bpr bayesian personalized ranking from implicit feedback"

Connected through hyperedge HE-6 (induced by source paper "wordrank learning word embeddings via robust ranking"):
  with members:
    - "a latent variable model approach to pmi based word embeddings"
    - "random walks on context spaces towards an explanation of the mysteries of semantic word embeddings"
    - "simlex 999 evaluating semantic models with genuine similarity estimation"
    - "recurrent models of visual attention"
    - "neural machine translation by jointly learning to align and translate"
    - "word representations via gaussian embedding"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-7 (induced by source paper "social collaborative retrieval"):
  with members:
    - "empirical analysis of predictive algorithms for collaborative filtering"
    - "collaborative topic regression with social matrix factorization for recommendation systems"

Connected through hyperedge HE-8 (induced by source paper "a parallel and efficient algorithm for learning to match"):
  with members:
    - "scaling up coordinate descent algorithms for large ell_1 regularization problems"
    - "parallel coordinate descent for l1 regularized loss 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.IR(Information Retrieval)