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 "complexity of manipulation with partial information in voting"):
  Abstract: The Coalitional Manipulation problem has been studied extensively in the literature for many voting rules. However, most studies have focused on the complete information setting, wherein the manipulators know the votes of the non-manipulators. While this assumption is reasonable for purposes of showing intractability, it is unrealistic for algorithmic considerations. In most real-world scenarios, it is impractical for the manipulators to have accurate knowledge of all the other votes. In this paper, we investigate manipulation with incomplete information. In our framework, the manipulators know a partial order for each voter that is consistent with the true preference of that voter. In this setting, we formulate three natural computational notions of manipulation, namely weak, opportunistic, and strong manipulation. We say that an extension of a partial order is if there exists a manipulative vote for that extension. #R##N#1. Weak Manipulation (WM): the manipulators seek to vote in a way that makes their preferred candidate win in at least one extension of the partial votes of the non-manipulators. #R##N#2. Opportunistic Manipulation (OM): the manipulators seek to vote in a way that makes their preferred candidate win in every viable extension of the partial votes of the non-manipulators. #R##N#3. Strong Manipulation (SM): the manipulators seek to vote in a way that makes their preferred candidate win in every extension of the partial votes of the non-manipulators. #R##N#We consider several scenarios for which the traditional manipulation problems are easy (for instance, Borda with a single manipulator). For many of them, the corresponding manipulative questions that we propose turn out to be computationally intractable. Our hardness results often hold even when very little information is missing, or in other words, even when the instances are quite close to the complete information setting.

Includes members:
  Member "manipulative elicitation a new attack on elections with incomplete preferences":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a parameterized perspective on protecting elections")
  Member "on the exact amount of missing information that makes finding possible winners hard":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the complexity of possible winners on partial chains")
  Member "complexity of manipulation in elections with partial votes":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "complexity of manipulative actions when voting with ties")
  Member "frugal bribery in voting":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a parameterized perspective on protecting elections")
  Member "detecting possible manipulators in elections":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a parameterized perspective on protecting elections")
  Member "kernelization complexity of possible winner and coalitional manipulation problems in voting":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "manipulating districts to win elections fine grained complexity")
  Member "the computational impact of partial votes on strategic voting":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "approximating voting rules from truncated ballots")
  Member "possible and necessary winner problem in social polls":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "manipulation is harder with incomplete votes")

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)