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: "markov chain methods for the bipartite boolean quadratic programming problem"
  Abstract: We study the Bipartite Boolean Quadratic Programming Problem (BBQP) which is an extension of the well known Boolean Quadratic Programming Problem (BQP). Applications of the BBQP include mining discrete patterns from binary data, approximating matrices by rank-one binary matrices, computing the cut-norm of a matrix, and solving optimisation problems such as maximum weight biclique, bipartite maximum weight cut, maximum weight induced sub-graph of a bipartite graph, etc. For the BBQP, we first present several algorithmic components, specifically, hill climbers and mutations, and then show how to combine them in a high-performance metaheuristic. Instead of hand-tuning a standard metaheuristic to test the efficiency of the hybrid of the components, we chose to use an automated generation of a multi-component metaheuristic to save human time, and also improve objectivity in the analysis and comparisons of components. For this we designed a new metaheuristic schema which we call Conditional Markov Chain Search (CMCS). We show that CMCS is flexible enough to model several standard metaheuristics; this flexibility is controlled by multiple numeric parameters, and so is convenient for automated generation. We study the configurations revealed by our approach and show that the best of them outperforms the previous state-of-the-art BBQP algorithm by several orders of magnitude. In our experiments we use benchmark instances introduced in the preliminary version of this paper and described here, which have already become the de facto standard in the BBQP literature.

Connected through hyperedge HE-1 (induced by source paper "conditional markov chain search for the generalised travelling salesman problem for warehouse order picking"):
  with members:
    - "conditional markov chain search for the simple plant location problem improves upper bounds on twelve korkel ghosh instances"
    - "algorithm configuration learning policies for the quick termination of poor performers"
    - "the generalized traveling salesman problem solved with ant algorithms"
    - "efficient local search algorithms for known and new neighborhoods for the generalized traveling salesman problem"
    - "lin kernighan heuristic adaptations for the generalized traveling salesman problem"
    - "a memetic algorithm for the generalized traveling salesman problem"
    - "empirical evaluation of construction heuristics for the multidimensional assignment problem"

Connected through hyperedge HE-2 (induced by source paper "hyperparameter optimisation with early termination of poor performers"):
  with members:
    - "algorithm configuration learning policies for the quick termination of poor performers"
    - "hyperband a novel bandit based approach to hyperparameter optimization"
    - "xgboost a scalable tree boosting system"

Connected through hyperedge HE-3 (induced by source paper "machine learning for combinatorial optimization a methodological tour d horizon"):
  with members:
    - "exact combinatorial optimization with graph convolutional neural networks"
    - "predicting tactical solutions to operational planning problems under imperfect information"
    - "learning to optimize neural nets"
    - "automated treatment planning in radiation therapy using generative adversarial networks"
    - "learning a sat solver from single bit supervision"
    - "deterministic implementations for reproducibility in deep reinforcement learning"
    - "generative adversarial networks an overview"
    - "learning permutations with sinkhorn policy gradient"

Connected through hyperedge HE-4 (induced by source paper "conditional markov chain search for the simple plant location problem improves upper bounds on twelve korkel ghosh instances"):
  with members:
    - "greedy facility location algorithms analyzed using dual fitting with factor revealing lp"

Connected through hyperedge HE-5 (induced by source paper "algorithm configuration learning policies for the quick termination of poor performers"):
  with members:
    - "conditional markov chain search for the simple plant location problem improves upper bounds on twelve korkel ghosh instances"

Connected through hyperedge HE-6 (induced by source paper "deep learning assisted heuristic tree search for the container pre marshalling problem"):
  with members:
    - "planning chemical syntheses with deep neural networks and symbolic ai"
    - "learning combinatorial optimization algorithms over graphs"
    - "a multi heuristic approach for solving the pre marshalling problem"
    - "neural combinatorial optimization with reinforcement learning"
    - "deepcoder learning to write programs"
    - "theano a python framework for fast computation of mathematical expressions"
    - "aslib a benchmark library for algorithm selection"
    - "learning to search better than your teacher"

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)