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: "a comparative study of matrix factorization and random walk with restart in recommender systems"
  Abstract: Between matrix factorization or Random Walk with Restart (RWR), which method works better for recommender systems? Which method handles explicit or implicit feedback data better? Does additional information help recommendation? Recommender systems play an important role in many e-commerce services such as Amazon and Netflix to recommend new items to a user. Among various recommendation strategies, collaborative filtering has shown good performance by using rating patterns of users. Matrix factorization and random walk with restart are the most representative collaborative filtering methods. However, it is still unclear which method provides better recommendation performance despite their extensive utility. #R##N#In this paper, we provide a comparative study of matrix factorization and RWR in recommender systems. We exactly formulate each correspondence of the two methods according to various tasks in recommendation. Especially, we newly devise an RWR method using global bias term which corresponds to a matrix factorization method using biases. We describe details of the two methods in various aspects of recommendation quality such as how those methods handle cold-start problem which typically happens in collaborative filtering. We extensively perform experiments over real-world datasets to evaluate the performance of each method in terms of various measures. We observe that matrix factorization performs better with explicit feedback ratings while RWR is better with implicit ones. We also observe that exploiting global popularities of items is advantageous in the performance and that side information produces positive synergy with explicit feedback but gives negative effects with implicit one.

Connected through hyperedge HE-1 (induced by source paper "adversarial attacks on linear contextual bandits"):
  with members:
    - "stochastic linear optimization with adversarial corruption"
    - "data poisoning attacks on stochastic bandits"
    - "corruption robust exploration in episodic reinforcement learning"
    - "better algorithms for stochastic bandits with adversarial corruptions"
    - "targeted attacks on deep reinforcement learning agents through adversarial observations"
    - "manipulating machine learning poisoning attacks and countermeasures for regression learning"
    - "data poisoning attacks in contextual bandits"
    - "adversarial bandits with knapsacks"

Connected through hyperedge HE-2 (induced by source paper "data context adaptation for accurate recommendation with additional information"):
  with members:
    - "integrating reviews into personalized ranking for cold start recommendation"
    - "neural collaborative filtering"
    - "neural network matrix factorization"
    - "adam a method for stochastic optimization"
    - "collaborative deep learning for recommender systems"

Connected through hyperedge HE-3 (induced by source paper "zoom svd fast and memory efficient method for extracting key patterns in an arbitrary time range"):
  with members:
    - "scalable tucker factorization for sparse tensors algorithms and discoveries"

Connected through hyperedge HE-4 (induced by source paper "tr svd fast and memory efficient method for time ranged singular value decomposition"):
  with members:
    - "scalable tucker factorization for sparse tensors algorithms and discoveries"

Connected through hyperedge HE-5 (induced by source paper "fast and accurate random walk with restart on dynamic graphs with guarantees"):
  with members:
    - "supervised and extended restart in random walks for ranking and link prediction in networks"
    - "tpa fast scalable and accurate method for approximate random walk with restart on billion scale graphs"
    - "approximate personalized pagerank on dynamic graphs"
    - "fast and exact top k search for random walk with restart"
    - "fast incremental and personalized pagerank"

Connected through hyperedge HE-6 (induced by source paper "tpa fast scalable and accurate method for approximate random walk with restart on billion scale graphs"):
  with members:
    - "personalized pagerank estimation and search a bidirectional approach"
    - "fast ppr scaling personalized pagerank estimation for large graphs"
    - "fast and exact top k search for random walk with restart"
    - "supervised random walks predicting and recommending links in social networks"

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)