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: "achieving fully proportional representation approximability results"
  Abstract: We study the complexity of (approximate) winner determination under the Monroe and Chamberlin--Courant multiwinner voting rules, which determine the set of representatives by optimizing the total (dis)satisfaction of the voters with their representatives. The total (dis)satisfaction is calculated either as the sum of individual (dis)satisfactions (the utilitarian case) or as the (dis)satisfaction of the worst off voter (the egalitarian case). We provide good approximation algorithms for the satisfaction-based utilitarian versions of the Monroe and Chamberlin--Courant rules, and inapproximability results for the dissatisfaction-based utilitarian versions of them and also for all egalitarian cases. Our algorithms are applicable and particularly appealing when voters submit truncated ballots. We provide experimental evaluation of the algorithms both on real-life preference-aggregation data and on synthetic data. These experiments show that our simple and fast algorithms can in many cases find near-perfect solutions.

Connected through hyperedge HE-1 (induced by source paper "representing the insincere strategically robust proportional representation"):
  with members:
    - "how long is a piece of string an exploration of multi winner approval voting and ballot length restrictions"
    - "consistent approval based multi winner rules"
    - "the expanding approvals rule improving proportional representation and monotonicity"
    - "the maximin support method an extension of the d hondt method to approval based multiwinner elections"

Connected through hyperedge HE-2 (induced by source paper "multiwinner analogues of plurality rule axiomatic and algorithmic perspectives"):
  with members:
    - "properties of multiwinner voting rules"
    - "finding a collective set of items from proportional multirepresentation to group recommendation"
    - "computational aspects of multi winner approval voting"
    - "on the computation of fully proportional representation"

Connected through hyperedge HE-3 (induced by source paper "properties of multiwinner voting rules"):
  with members:
    - "finding a collective set of items from proportional multirepresentation to group recommendation"
    - "justified representation in approval based committee voting"
    - "computational aspects of multi winner approval voting"
    - "on the computation of fully proportional representation"
    - "the complexity of fully proportional representation for single crossing electorates"
    - "fully proportional representation as resource allocation approximability results"
    - "exact complexity of the winner problem for young elections"
    - "exact analysis of dodgson elections lewis carroll s 1876 voting system is complete for parallel access to np"

Connected through hyperedge HE-4 (induced by source paper "justified representation in approval based committee voting"):
  with members:
    - "proportional justified representation"
    - "structure in dichotomous preferences"
    - "properties of multiwinner voting rules"
    - "ptas for minimax approval voting"
    - "finding a collective set of items from proportional multirepresentation to group recommendation"
    - "computational aspects of multi winner approval voting"
    - "on the computation of fully proportional representation"
    - "the complexity of fully proportional representation for single crossing electorates"

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.AI(Artificial Intelligence)