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: "sample complexity for winner prediction in elections"
  Abstract: Predicting the winner of an election is a favorite problem both for news media pundits and computational social choice theorists. Since it is often infeasible to elicit the preferences of all the voters in a typical prediction scenario, a common algorithm used for winner prediction is to run the election on a small sample of randomly chosen votes and output the winner as the prediction. We analyze the performance of this algorithm for many common voting rules. #R##N#More formally, we introduce the $(\epsilon, \delta)$-winner determination problem, where given an election on $n$ voters and $m$ candidates in which the margin of victory is at least $\epsilon n$ votes, the goal is to determine the winner with probability at least $1-\delta$. The margin of victory of an election is the smallest number of votes that need to be modified in order to change the election winner. We show interesting lower and upper bounds on the number of samples needed to solve the $(\epsilon, \delta)$-winner determination problem for many common voting rules, including scoring rules, approval, maximin, Copeland, Bucklin, plurality with runoff, and single transferable vote. Moreover, the lower and upper bounds match for many common voting rules in a wide range of practically appealing scenarios.

Connected through hyperedge HE-1 (induced by source paper "sybil resilient social choice with partial participation"):
  with members:
    - "sybil resilient conductance based community growth"
    - "sybil resilient reality aware social choice"
    - "incorporating reality into social choice"
    - "proxy voting for better outcomes"
    - "equilibria of plurality voting with abstentions"

Connected through hyperedge HE-2 (induced by source paper "a learning framework for distribution based game theoretic solution concepts"):
  with members:
    - "forming probably stable communities with limited interactions"
    - "envy free classification"
    - "statistical cost sharing"
    - "a sample complexity measure with applications to learning optimal auctions"
    - "hedonic games with graph restricted communication"
    - "learning simple auctions"
    - "sample complexity of automated mechanism design"
    - "learning cooperative games"

Connected through hyperedge HE-3 (induced by source paper "distributed monitoring of election winners"):
  with members:
    - "comparing election methods where each voter ranks only few candidates"
    - "optimal random sampling from distributed streams revisited"
    - "proportional representation in vote streams"
    - "optimizing positional scoring rules for rank aggregation"
    - "fishing out winners from vote streams"
    - "estimating the margin of victory of an election using sampling"
    - "crowdsourcing for participatory democracies efficient elicitation of social choice functions"
    - "determining possible and necessary winners under common voting rules given partial orders"

Connected through hyperedge HE-4 (induced by source paper "proportional representation in vote streams"):
  with members:
    - "distributed monitoring of election winners"
    - "an optimal algorithm for l1 heavy hitters in insertion streams and related problems"
    - "better streaming algorithms for the maximum coverage problem"
    - "fishing out winners from vote streams"
    - "properties of multiwinner voting rules"
    - "finding a collective set of items from proportional multirepresentation to group recommendation"
    - "on the computation of fully proportional representation"
    - "fully proportional representation as resource allocation approximability results"

Connected through hyperedge HE-5 (induced by source paper "an optimal algorithm for l1 heavy hitters in insertion streams and related problems"):
  with members:
    - "bptree an ell_2 heavy hitters algorithm using constant memory"
    - "new algorithms for heavy hitters in data streams"
    - "beating countsketch for heavy hitters in insertion streams"
    - "analysis of a bloom filter algorithm via the supermarket model"
    - "medians and beyond new aggregation techniques for sensor networks"

Connected through hyperedge HE-6 (induced by source paper "on choosing committees based on approval votes in the presence of outliers"):
  with members:
    - "ptas for minimax approval voting"
    - "justified representation in approval based committee voting"
    - "on approximating string selection problems with outliers"
    - "multimode control attacks on elections"
    - "a richer understanding of the complexity of election systems"
    - "anyone but him the complexity of precluding an alternative"
    - "exact complexity of the winner problem for young elections"

Connected through hyperedge HE-7 (induced by source paper "fishing out winners from vote streams"):
  with members:
    - "crowdsourcing for participatory democracies efficient elicitation of social choice functions"
    - "tight bounds for lp samplers finding duplicates in streams and related problems"

Connected through hyperedge HE-8 (induced by source paper "estimating the margin of victory of an election using sampling"):
  with members:
    - "how hard is bribery in 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.DS(Data Structures and Algorithms)