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: "exploring trust aware neighbourhood in trust based recommendation"
  Abstract: Traditional Recommender Systems (RS) do not consider any personal user information beyond rating history. Such information, on the other hand, is widely available on social networking sites (Facebook, Twitter). As a result, social networks have recently been used in recommendation systems. In this paper, we propose an efficient method for incorporating social signals into the recommendation process by building a trust network which supplements the users' rating profiles. We first show the effect of different cold-start users types on the Collaborative Filtering (CF) technique in several real-world datasets. Later, we propose a "Trust-Aware Neighbourhood" algorithm which addresses a performance issue of the former by limiting the trusted neighbourhood. We show the doubling of the rating coverage compared to the traditional CF technique, and a significant improvement in the accuracy for some datasets. Focusing specifically on cold-start users, we propose a "Hybrid Trust-Aware Neighbourhood" algorithm which expands the neighbourhood by considering both trust and rating history of the users. We show a near complete coverage with a rich trust network dataset-- Flixster. We conclude by discussing the potential implementation of this algorithm in a budget-constrained cloud environment.

Connected through hyperedge HE-1 (induced by source paper "almost boltzmann exploration"):
  with members:
    - "thompson sampling for stochastic bandits with graph feedback"
    - "boltzmann exploration done right"
    - "nonstochastic multi armed bandits with graph structured feedback"
    - "leveraging side observations in stochastic bandits"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "from bandits to experts on the value of side observations"

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)