human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "manipulative elicitation a new attack on elections with incomplete preferences"):
  Abstract: Lu and Boutilier proposed a novel approach based on "minimax regret" to use classical score based voting rules in the setting where preferences can be any partial (instead of complete) orders over the set of alternatives. We show here that such an approach is vulnerable to a new kind of manipulation which was not present in the classical (where preferences are complete orders) world of voting. We call this attack "manipulative elicitation." More specifically, it may be possible to (partially) elicit the preferences of the agents in a way that makes some distinguished alternative win the election who may not be a winner if we elicit every preference completely. More alarmingly, we show that the related computational task is polynomial time solvable for a large class of voting rules which includes all scoring rules, maximin, Copeland$^\alpha$ for every $\alpha\in[0,1]$, simplified Bucklin voting rules, etc. We then show that introducing a parameter per pair of alternatives which specifies the minimum number of partial preferences where this pair of alternatives must be comparable makes the related computational task of manipulative elicitation \NPC for all common voting rules including a class of scoring rules which includes the plurality, $k$-approval, $k$-veto, veto, and Borda voting rules, maximin, Copeland$^\alpha$ for every $\alpha\in[0,1]$, and simplified Bucklin voting rules. Hence, in this work, we discover a fundamental vulnerability in using minimax regret based approach in partial preferential setting and propose a novel way to tackle it.

Includes members:
  Member "on the exact amount of missing information that makes finding possible winners hard":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the complexity of possible winners on partial chains")
  Member "elicitation for preferences single peaked on trees":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "recognizing and eliciting weakly single crossing profiles on trees")
  Member "preference elicitation for single crossing domain":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "testing preferential domains using sampling")
  Member "query complexity of tournament solutions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "refining tournament solutions via margin of victory")
  Member "complexity of manipulation with partial information in voting":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "the complexity of possible winners on partial chains")
  Member "estimating the margin of victory of an election using sampling":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "minimizing margin of victory for fair political and educational districting")
  Member "frugal bribery in voting":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a parameterized perspective on protecting elections")
  Member "detecting possible manipulators in elections":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a parameterized perspective on protecting elections")

Question: Please tell me which class the source paper 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.MA(Multiagent Systems)