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: "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.

Connected through hyperedge HE-1 (induced by source paper "the complexity of possible winners on partial chains"):
  with members:
    - "computational social choice meets databases"
    - "the complexity of the possible winner problem over partitioned preferences"
    - "on the exact amount of missing information that makes finding possible winners hard"
    - "riffled independence for efficient inference with partial rankings"
    - "determining possible and necessary winners under common voting rules given partial orders"
    - "taking the final step to a full dichotomy of the possible winner problem in pure scoring rules"
    - "complexity of and algorithms for borda manipulation"
    - "towards a dichotomy for the possible winner problem in elections based on scoring rules"

Connected through hyperedge HE-2 (induced by source paper "manipulating districts to win elections fine grained complexity"):
  with members:
    - "optimal parameterized algorithms for planar facility location problems using voronoi diagrams"
    - "kernelization complexity of possible winner and coalitional manipulation problems in voting"
    - "the complexity of manipulative attacks in nearly single peaked electorates"
    - "anyone but him the complexity of precluding an alternative"

Connected through hyperedge HE-3 (induced by source paper "a parameterized perspective on protecting elections"):
  with members:
    - "optimal bribery in voting"
    - "manipulative elicitation a new attack on elections with incomplete preferences"
    - "on the exact amount of missing information that makes finding possible winners hard"
    - "frugal bribery in voting"
    - "elections with few voters candidate control can be easy"
    - "detecting possible manipulators in elections"
    - "kernelization complexity of possible winner and coalitional manipulation problems in voting"
    - "more natural models of electoral control by partition"

Connected through hyperedge HE-4 (induced by source paper "stable manipulation in voting"):
  with members:
    - "local distance restricted bribery in voting"
    - "dominating manipulations in voting with partial information"

Connected through hyperedge HE-5 (induced by source paper "manipulative elicitation a new attack on elections with incomplete preferences"):
  with members:
    - "on the exact amount of missing information that makes finding possible winners hard"
    - "elicitation for preferences single peaked on trees"
    - "preference elicitation for single crossing domain"
    - "query complexity of tournament solutions"
    - "estimating the margin of victory of an election using sampling"
    - "frugal bribery in voting"
    - "detecting possible manipulators in elections"
    - "kernelization complexity of possible winner and coalitional manipulation problems in voting"

Connected through hyperedge HE-6 (induced by source paper "frugal bribery in voting"):
  with members:
    - "on the hardness of bribery variants in voting with cp nets"
    - "prices matter for the parameterized complexity of shift bribery"
    - "campaign management under approval driven voting rules"
    - "kernelization complexity of possible winner and coalitional manipulation problems in voting"
    - "complexity of manipulation bribery and campaign management in bucklin and fallback voting"
    - "dominating manipulations in voting with partial information"
    - "complexity of and algorithms for borda manipulation"
    - "multimode control attacks on elections"

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.MA(Multiagent Systems)