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: "a complete characterization of statistical query learning with applications to evolvability"
  Abstract: Statistical query (SQ) learning model of Kearns (1993) is a natural restriction of the PAC learning model in which a learning algorithm is allowed to obtain estimates of statistical properties of the examples but cannot see the examples themselves. We describe a new and simple characterization of the query complexity of learning in the SQ learning model. Unlike the previously known bounds on SQ learning our characterization preserves the accuracy and the efficiency of learning. The preservation of accuracy implies that that our characterization gives the first characterization of SQ learning in the agnostic learning framework. The preservation of efficiency is achieved using a new boosting technique and allows us to derive a new approach to the design of evolutionary algorithms in Valiant's (2006) model of evolvability. We use this approach to demonstrate the existence of a large class of monotone evolutionary learning algorithms based on square loss performance estimation. These results differ significantly from the few known evolutionary algorithms and give evidence that evolvability in Valiant's model is a more versatile phenomenon than there had been previous reason to suspect.

Connected through hyperedge HE-1 (induced by source paper "computational complexity analysis of genetic programming"):
  with members:
    - "evolving boolean functions with conjunctions and disjunctions via genetic programming"
    - "on inversely proportional hypermutations with mutation potential"
    - "where are we now a large benchmark study of recent symbolic regression methods"
    - "bounding bloat in genetic programming"
    - "destructiveness of lexicographic parsimony pressure and alleviation by a concatenation crossover in genetic programming"
    - "standard steady state genetic algorithms can hillclimb faster than mutation only evolutionary algorithms"
    - "level based analysis of genetic algorithms and other search processes"
    - "computational complexity analysis of multi objective genetic programming"

Connected through hyperedge HE-2 (induced by source paper "towards a combinatorial characterization of bounded memory learning"):
  with members:
    - "memory sample tradeoffs for linear regression with small error"
    - "space lower bounds for linear prediction in the streaming model"
    - "detecting correlations with little memory and communication"
    - "extractor based time space lower bounds for learning"
    - "a general memory bounded learning algorithm"
    - "fast learning requires good memory a time space lower bound for parity learning"
    - "fundamental limits of online and distributed algorithms for statistical learning and estimation"

Connected through hyperedge HE-3 (induced by source paper "distribution free evolvability of vector spaces all it takes is a generating set"):
  with members:
    - "distribution independent evolvability of linear threshold functions"
    - "evolution with drifting targets"

Connected through hyperedge HE-4 (induced by source paper "a general characterization of the statistical query complexity"):
  with members:
    - "on the power of learning from k wise queries"
    - "statistical query algorithms for mean vector estimation and stochastic convex optimization"
    - "generalization in adaptive data analysis and holdout reuse"
    - "algorithmic stability for adaptive data analysis"
    - "rappor randomized aggregatable privacy preserving ordinal response"
    - "preserving statistical validity in adaptive data analysis"
    - "approximate resilience monotonicity and the complexity of agnostic learning"
    - "statistical active learning algorithms for noise tolerance and differential privacy"

Connected through hyperedge HE-5 (induced by source paper "statistical query algorithms for stochastic convex optimization"):
  with members:
    - "escaping the local minima via simulated annealing optimization of approximately convex functions"
    - "generalization in adaptive data analysis and holdout reuse"
    - "algorithmic stability for adaptive data analysis"
    - "sum of squares lower bounds for planted clique"
    - "preventing false discovery in interactive data analysis is hard"
    - "lower bounds on the size of semidefinite programming relaxations"
    - "preserving statistical validity in adaptive data analysis"
    - "private multiplicative weights beyond linear queries"

Connected through hyperedge HE-6 (induced by source paper "approximate resilience monotonicity and the complexity of agnostic learning"):
  with members:
    - "agnostic learning of disjunctions on symmetric distributions"
    - "on the complexity of random satisfiability problems with planted solutions"
    - "the complexity of learning halfspaces using generalized linear methods"
    - "minimizing the misclassification error rate using a surrogate convex loss"
    - "submodular functions are noise stable"
    - "agnostic learning of monomials by halfspaces is hard"
    - "approximation resistant predicates from pairwise independence"

Connected through hyperedge HE-7 (induced by source paper "agnostic learning of disjunctions on symmetric distributions"):
  with members:
    - "approximate resilience monotonicity and the complexity of agnostic learning"
    - "representation approximation and learning of submodular functions using low rank decision trees"
    - "learning coverage functions and private release of marginals"
    - "agnostic learning of monomials by halfspaces is hard"
    - "privately releasing conjunctions and the statistical query barrier"
    - "distribution specific agnostic boosting"

Connected through hyperedge HE-8 (induced by source paper "learning coverage functions and private release of marginals"):
  with members:
    - "agnostic learning of disjunctions on symmetric distributions"
    - "faster private release of marginals on small databases"
    - "submodular optimization with submodular cover and submodular knapsack constraints"
    - "optimal bounds on approximation of submodular and xos functions by juntas"
    - "efficient algorithms for privately releasing marginals via convex relaxations"
    - "representation approximation and learning of submodular functions using low rank decision trees"
    - "fingerprinting codes and the price of approximate differential privacy"
    - "testing coverage functions"

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.CC(Computational Complexity)