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: "proportional approval method using squared loads approval removal and coin flip approval transformation pamsac a new system of proportional representation using approval voting"
  Abstract: Several multi-winner systems that use approval voting have been developed but they each suffer from various problems. Six of these methods are discussed in this paper. They are Satisfaction Approval Voting, Minimax Approval Voting, Proportional Approval Voting, Monroe's Fully Proportional Representation, Chamberlin-Courant's Rule, and Ebert's method. They all fail at least one of Proportional Representation (PR), strong PR, monotonicity or positive support. However, the new method described in this paper - Proportional Approval Method using Squared loads, Approval removal and Coin-flip approval transformation (PAMSAC) - passes them all. PAMSAC uses the squared loads of Ebert's method, but removes non-beneficial approvals to restore monotonicity. It also uses the Coin-Flip Approval Transformation (CFAT), where voters are "split" into two for each candidate they approve, and where one half of this split voter approves and the other half does not approve each candidate approved on the ballot. This restores positive support, and also makes the method equivalent to the D'Hondt party-list method for party voting. PAMSAC reduces to simple approval voting in the single-winner case. A score voting version is described that also reduces to simple score voting in the single-winner case.

Connected through hyperedge HE-1 (induced by source paper "committee selection with attribute level preferences"):
  with members:
    - "multi attribute proportional representation"
    - "campaign management under approval driven voting rules"
    - "justified representation in approval based committee voting"
    - "computational aspects of multi winner approval voting"

Connected through hyperedge HE-2 (induced by source paper "axiomatic characterization of committee scoring rules"):
  with members:
    - "what do we elect committees for a voting committee model for multi winner rules"
    - "multiwinner approval rules as apportionment methods"
    - "consistent probabilistic social choice"
    - "properties of multiwinner voting rules"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "multiwinner approval rules as apportionment methods"):
  with members:
    - "proportional justified representation"
    - "properties of multiwinner voting rules"
    - "finding a collective set of items from proportional multirepresentation to group recommendation"
    - "justified representation in approval based committee voting"

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.GT(Computer Science and Game Theory)