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: "ultra accurate collaborative information filtering via directed user similarity"
  Abstract: A key challenge of the collaborative filtering (CF) information filtering is how to obtain the reliable and accurate results with the help of peers' recommendation. Since the similarities from small-degree users to large-degree users would be larger than the ones in opposite direction, the large-degree users' selections are recommended extensively by the traditional second-order CF algorithms. By considering the users' similarity direction and the second-order correlations to depress the influence of mainstream preferences, we present the directed second-order CF (HDCF) algorithm specifically to address the challenge of accuracy and diversity of the CF algorithm. The numerical results for two benchmark data sets, MovieLens and Netflix, show that the accuracy of the new algorithm outperforms the state-of-the-art CF algorithms. Comparing with the CF algorithm based on random walks proposed by Liu et al. (Int. J. Mod. Phys. C, 20 (2009) 285) the average ranking score could reach 0.0767 and 0.0402, which is enhanced by 27.3% and 19.1% for MovieLens and Netflix, respectively. In addition, the diversity, precision and recall are also enhanced greatly. Without relying on any context-specific information, tuning the similarity direction of CF algorithms could obtain accurate and diverse recommendations. This work suggests that the user similarity direction is an important factor to improve the personalized recommendation performance.

Connected through hyperedge HE-1 (induced by source paper "a trust based recommendation method using network diffusion processes"):
  with members:
    - "evaluating user reputation in online rating systems via an iterative group based ranking method"
    - "a vertex similarity index for better personalized recommendation"
    - "network based recommendation algorithms a review"
    - "group based ranking method for online rating systems with spamming attacks"
    - "multi linear interactive matrix factorization"
    - "information filtering by similarity preferential diffusion processes"
    - "solving the apparent diversity accuracy dilemma of recommender systems"

Connected through hyperedge HE-2 (induced by source paper "coarse cluster enhancing collaborative recommendation for social network systems"):
  with members:
    - "personalized recommendation with corrected similarity"
    - "information filtering via collaborative user clustering modeling"
    - "uncovering the information core in recommender systems"
    - "tag aware recommender systems a state of the art survey"
    - "collaborative filtering with diffusion based similarity on tripartite graphs"
    - "solving the apparent diversity accuracy dilemma of recommender systems"
    - "personalized recommendation via integrated diffusion on user item tag tripartite graphs"

Connected through hyperedge HE-3 (induced by source paper "a vertex similarity index for better personalized recommendation"):
  with members:
    - "evaluating user reputation in online rating systems via an iterative group based ranking method"
    - "group based ranking method for online rating systems with spamming attacks"
    - "information filtering by similarity preferential diffusion processes"
    - "extracting the information backbone in online system"
    - "solving the apparent diversity accuracy dilemma of recommender systems"

Connected through hyperedge HE-4 (induced by source paper "predicting the evolution of complex networks via similarity dynamics"):
  with members:
    - "full scale cascade dynamics prediction with a local first approach"
    - "the lifecycle and cascade of wechat social messaging groups"
    - "can cascades be predicted"
    - "integrated structure investigation in complex networks by label propagation"
    - "the rich club phenomenon in the internet topology"

Connected through hyperedge HE-5 (induced by source paper "group based ranking method for online rating systems with spamming attacks"):
  with members:
    - "information filtering by similarity preferential diffusion processes"
    - "a robust ranking algorithm to spamming"
    - "iterative filtering for a dynamical reputation system"

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)