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: "playing mastermind with many colors"
  Abstract: We analyze the general version of the classic guessing game Mastermind with $n$ positions and $k$ colors. Since the case $k \le n^{1-\varepsilon}$, $\varepsilon>0$ a constant, is well understood, we concentrate on larger numbers of colors. For the most prominent case $k = n$, our results imply that Codebreaker can find the secret code with $O(n \log \log n)$ guesses. This bound is valid also when only black answer-pegs are used. It improves the $O(n \log n)$ bound first proven by Chv\'atal (Combinatorica 3 (1983), 325--329). We also show that if both black and white answer-pegs are used, then the $O(n \log\log n)$ bound holds for up to $n^2 \log\log n$ colors. These bounds are almost tight as the known lower bound of $\Omega(n)$ shows. Unlike for $k \le n^{1-\varepsilon}$, simply guessing at random until the secret code is determined is not sufficient. In fact, we show that an optimal non-adaptive strategy (deterministic or randomized) needs $\Theta(n \log n)$ guesses.

Connected through hyperedge HE-1 (induced by source paper "the advantages of multiple classes for reducing overfitting from test set reuse"):
  with members:
    - "do imagenet classifiers generalize to imagenet"
    - "do cifar 10 classifiers generalize to cifar 10"
    - "the ladder a reliable leaderboard for machine learning competitions"
    - "generalization in adaptive data analysis and holdout reuse"
    - "algorithmic stability for adaptive data analysis"
    - "preventing false discovery in interactive data analysis is hard"
    - "imagenet large scale visual recognition challenge"
    - "preserving statistical validity in adaptive data analysis"

Connected through hyperedge HE-2 (induced by source paper "optimal multiclass overfitting by sequence reconstruction from hamming queries"):
  with members:
    - "cold case the lost mnist digits"
    - "the advantages of multiple classes for reducing overfitting from test set reuse"
    - "do imagenet classifiers generalize to imagenet"
    - "do cifar 10 classifiers generalize to cifar 10"
    - "the ladder a reliable leaderboard for machine learning competitions"
    - "generalization in adaptive data analysis and holdout reuse"
    - "algorithmic stability for adaptive data analysis"
    - "preserving statistical validity in adaptive data analysis"

Connected through hyperedge HE-3 (induced by source paper "the query complexity of mastermind with ell_p distances"):
  with members:
    - "the query complexity of a permutation based variant of mastermind"

Connected through hyperedge HE-4 (induced by source paper "the query complexity of a permutation based variant of mastermind"):
  with members:
    - "complexity theory for discrete black box optimization heuristics"
    - "the 1 1 elitist black box complexity of leadingones"
    - "faster black box algorithms through higher arity operators"

Connected through hyperedge HE-5 (induced by source paper "standard steady state genetic algorithms can hillclimb faster than mutation only evolutionary algorithms"):
  with members:
    - "fast genetic algorithms"
    - "escaping local optima using crossover with emergent or reinforced diversity"
    - "the right mutation strength for multi valued decision variables"
    - "optimal parameter choices through self adjustment applying the 1 5 th rule in discrete settings"
    - "a new method for lower bounds on the running time of evolutionary algorithms"
    - "playing mastermind with constant size memory"

Connected through hyperedge HE-6 (induced by source paper "on the query complexity of black peg ab mastermind"):
  with members:
    - "the worst case number of questions in generalized ab game with and without white peg answers"
    - "improved approximation algorithm for the number of queries necessary to identify a permutation"
    - "hardness of mastermind"
    - "playing mastermind with constant size memory"
    - "on the algorithmic complexity of the mastermind game with black peg results"
    - "the mastermind attack on genomic data"
    - "mastermind is np complete"

Connected through hyperedge HE-7 (induced by source paper "the worst case number of questions in generalized ab game with and without white peg answers"):
  with members:
    - "improved approximation algorithm for the number of queries necessary to identify a permutation"
    - "hardness of mastermind"
    - "playing mastermind with constant size memory"
    - "on the algorithmic complexity of the mastermind game with black peg results"
    - "mastermind is np complete"

Connected through hyperedge HE-8 (induced by source paper "improved approximation algorithm for the number of queries necessary to identify a permutation"):
  with members:
    - "hardness of mastermind"
    - "playing mastermind with constant size memory"
    - "on the algorithmic complexity of the mastermind game with black peg results"
    - "mastermind is np complete"

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)