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: "on the exact amount of missing information that makes finding possible winners hard"
  Abstract: We consider election scenarios with incomplete information, a situation that arises often in practice. There are several models of incomplete information and accordingly, different notions of outcomes of such elections. In one well-studied model of incompleteness, the votes are given by partial orders over the candidates. In this context we can frame the problem of finding a possible winner, which involves determining whether a given candidate wins in at least one completion of a given set of partial votes for a specific voting rule. #R##N#The possible winner problem is well-known to be NP-complete in general, and it is in fact known to be NP-complete for several voting rules where the number of undetermined pairs in every vote is bounded only by some constant. In this paper, we address the question of determining precisely the smallest number of undetermined pairs for which the possible winner problem remains NP-complete. In particular, we find the exact values of $t$ for which the possible winner problem transitions to being NP-complete from being in P, where $t$ is the maximum number of undetermined pairs in every vote. We demonstrate tight results for a broad subclass of scoring rules which includes all the commonly used scoring rules (such as plurality, veto, Borda, $k$-approval, and so on), Copeland$^\alpha$ for every $\alpha\in[0,1]$, maximin, and Bucklin voting rules. A somewhat surprising aspect of our results is that for many of these rules, the possible winner problem turns out to be hard even if every vote has at most one undetermined pair of candidates.

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"
    - "complexity of manipulation with partial information in voting"
    - "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 "a parameterized perspective on protecting elections"):
  with members:
    - "optimal bribery in voting"
    - "manipulative elicitation a new attack on elections with incomplete preferences"
    - "complexity of manipulation with partial information in voting"
    - "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-3 (induced by source paper "the complexity of the possible winner problem over partitioned preferences"):
  with members:
    - "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"
    - "towards a dichotomy for the possible winner problem in elections based on scoring rules"
    - "dichotomy for voting systems"

Connected through hyperedge HE-4 (induced by source paper "manipulative elicitation a new attack on elections with incomplete preferences"):
  with members:
    - "elicitation for preferences single peaked on trees"
    - "preference elicitation for single crossing domain"
    - "query complexity of tournament solutions"
    - "complexity of manipulation with partial information in voting"
    - "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-5 (induced by source paper "complexity of manipulation with partial information in voting"):
  with members:
    - "manipulative elicitation a new attack on elections with incomplete preferences"
    - "complexity of manipulation in elections with partial votes"
    - "frugal bribery in voting"
    - "detecting possible manipulators in elections"
    - "kernelization complexity of possible winner and coalitional manipulation problems in voting"
    - "the computational impact of partial votes on strategic voting"
    - "possible and necessary winner problem in social polls"
    - "asymptotic collusion proofness of voting rules the case of large number of candidates"

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)