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: "pagerank optimization by edge selection"
  Abstract: The importance of a node in a directed graph can be measured by its PageRank. The PageRank of a node is used in a number of application contexts-including ranking websites-and can be interpreted as the average portion of time spent at the node by an infinite random walk. We consider the problem of maximizing the PageRank of a node by selecting some of the edges from a set of edges that are under our control. By applying results from Markov decision theory, we show that an optimal solution to this problem can be found in polynomial time. Our core solution results in a linear programming formulation, but we also provide an alternative greedy algorithm, a variant of policy iteration, which runs in polynomial time, as well. Finally, we show that, under the slight modification for which we are given mutually exclusive pairs of edges, the problem of PageRank optimization becomes NP-hard.

Connected through hyperedge HE-1 (induced by source paper "prince provider side interpretability with counterfactual explanations in recommender systems"):
  with members:
    - "jointly learning explainable rules for recommendation with knowledge graph"
    - "fairy a framework for understanding relationships between users actions and their social feeds"
    - "reinforcement knowledge graph reasoning for explainable recommendation"
    - "learning heterogeneous knowledge base embeddings for explainable recommendation"
    - "ripplenet propagating user preferences on the knowledge graph for recommender systems"
    - "explainable reasoning over knowledge graphs for recommendation"
    - "explainable recommendation via multi task learning in opinionated text data"
    - "explainable recommendation a survey and new perspectives"

Connected through hyperedge HE-2 (induced by source paper "certifiable robustness to graph perturbations"):
  with members:
    - "transferring robustness for graph neural network against poisoning attacks"
    - "adversarial examples on graph data deep insights into attack and defense"
    - "topology attack and defense for graph neural networks an optimization perspective"
    - "adversarial robustness of similarity based link prediction"
    - "adversarial defense framework for graph neural network"
    - "simplifying graph convolutional networks"
    - "learning graph neural networks with noisy labels"
    - "fast graph representation learning with pytorch geometric"

Connected through hyperedge HE-3 (induced by source paper "multiagent evaluation under incomplete information"):
  with members:
    - "emergent coordination through competition"
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "a generalised method for empirical game theoretic analysis"
    - "re evaluating evaluation"
    - "a unified game theoretic approach to multiagent reinforcement learning"
    - "population based training of neural networks"
    - "giraffe using deep reinforcement learning to play chess"
    - "learning equilibria of games via payoff queries"

Connected through hyperedge HE-4 (induced by source paper "adversarial attacks on node embeddings via graph poisoning"):
  with members:
    - "adversarial attacks on graph neural networks via meta learning"
    - "certifiable robustness and robust training for graph convolutional networks"
    - "adversarial attack on graph structured data"
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "deep text classification can be fooled"
    - "towards crafting text adversarial samples"
    - "tactics of adversarial attack on deep reinforcement learning agents"
    - "disabling external influence in social networks via edge recommendation"

Connected through hyperedge HE-5 (induced by source paper "the robustness of democratic consensus"):
  with members:
    - "gossips and prejudices ergodic randomized dynamics in social networks"
    - "opinion fluctuations and disagreement in social networks"
    - "distributed randomized algorithms for the pagerank computation"

Connected through hyperedge HE-6 (induced by source paper "improved bound on the worst case complexity of policy iteration"):
  with members:
    - "a complexity analysis of policy iteration through combinatorial matrices arising from unique sink orientations"
    - "the value iteration algorithm is not strongly polynomial for discounted dynamic programming"
    - "on the complexity of policy iteration"
    - "the simplex method is strongly polynomial for deterministic markov decision processes"
    - "strategy iteration is strongly polynomial for 2 player turn based stochastic games with a constant discount factor"
    - "exponential lower bounds for policy iteration"

Connected through hyperedge HE-7 (induced by source paper "ergodic randomized algorithms and dynamics over networks"):
  with members:
    - "almost sure convergence of a randomized algorithm for relative localization in sensor networks"
    - "convergence of distributed randomized pagerank algorithms"
    - "gossips and prejudices ergodic randomized dynamics in social networks"
    - "limited benefit of cooperation in distributed relative localization"
    - "opinion fluctuations and disagreement in social networks"
    - "distributed randomized algorithms for the pagerank computation"
    - "a web aggregation approach for distributed randomized pagerank algorithms"
    - "error scaling laws for linear optimal estimation from relative measurements"

Connected through hyperedge HE-8 (induced by source paper "the pagerank problem multi agent consensus and web aggregation a systems and control viewpoint"):
  with members:
    - "convergence of distributed randomized pagerank algorithms"
    - "distributed randomized algorithms for the pagerank computation"
    - "a web aggregation approach for distributed randomized pagerank algorithms"
    - "pagerank standing on the shoulders of giants"
    - "co authorship networks in the digital library research community"

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.DS(Data Structures and Algorithms)