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: "feature based factorized bilinear similarity model for cold start top n item recommendation"
  Abstract: Recommending new items to existing users has remained a challenging problem due to absence of user's past preferences for these items. The user personalized non-collaborative methods based on item features can be used to address this item cold-start problem. These methods rely on similarities between the target item and user's previous preferred items. While computing similarities based on item features, these methods overlook the interactions among the features of the items and consider them independently. Modeling interactions among features can be helpful as some features, when considered together, provide a stronger signal on the relevance of an item when compared to case where features are considered independently. To address this important issue, in this work we introduce the Feature-based factorized Bilinear Similarity Model (FBSM), which learns factorized bilinear similarity model for TOP-n recommendation of new items, given the information about items preferred by users in past as well as the features of these items. We carry out extensive empirical evaluations on benchmark datasets, and we find that the proposed FBSM approach improves upon traditional non-collaborative methods in terms of recommendation performance. Moreover, the proposed approach also learns insightful interactions among item features from data, which lead to deep understanding on how these interactions contribute to personalized recommendation.

Connected through hyperedge HE-1 (induced by source paper "eigenrec generalizing puresvd for effective and efficient top n recommendations"):
  with members:
    - "unbiased learning to rank with biased feedback"
    - "primme_svds a high performance preconditioned svd solver for accurate large scale computations"
    - "recommendations as treatments debiasing learning and evaluation"
    - "a preconditioned hybrid svd method for accurately computing singular triplets of large matrices"
    - "challenging the long tail recommendation"

Connected through hyperedge HE-2 (induced by source paper "a collective variational autoencoder for top n recommendation with side information"):
  with members:
    - "deep learning based recommender system a survey and new perspectives"
    - "neural collaborative filtering"
    - "a neural autoregressive approach to collaborative filtering"
    - "hybrid recommender system based on autoencoders"
    - "collaborative deep learning for recommender systems"
    - "a generalized mean field algorithm for variational inference in exponential families"

Connected through hyperedge HE-3 (induced by source paper "deriving item features relevance from collaborative domain knowledge"):
  with members:
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "semi supervised collaborative ranking with push at top"):
  with members:
    - "top rank optimization in linear time"
    - "the power of convex relaxation near optimal matrix completion"

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)