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: "algorithm selection for combinatorial search problems a survey"
  Abstract: The Algorithm Selection Problem is concerned with selecting the best algorithm to solve a given problem on a case-by-case basis. It has become especially relevant in the last decade, as researchers are increasingly investigating how to identify the most suitable existing algorithm for solving a problem instead of developing new algorithms. This survey presents an overview of this work focusing on the contributions made in the area of combinatorial search problems, where Algorithm Selection techniques have achieved significant performance improvements. We unify and organise the vast literature according to criteria that determine Algorithm Selection systems in practice. The comprehensive classification of approaches identifies and analyses the different directions from which Algorithm Selection has been approached. This paper contrasts and compares different methods for solving the problem as well as ways of using these solutions. It closes by identifying directions of current and future research.

Connected through hyperedge HE-1 (induced by source paper "lossless compression of deep neural networks"):
  with members:
    - "mipaal mixed integer program as a layer"
    - "caql continuous action q learning"
    - "function approximation by deep networks"
    - "equivalent and approximate transformations of deep neural networks"
    - "differentiable convex optimization layers"
    - "accelerating primal solution findings for mixed integer programs based on solution prediction"
    - "reinforcement learning for integer programming learning to cut"
    - "machine learning for combinatorial optimization a methodological tour d horizon"

Connected through hyperedge HE-2 (induced by source paper "data efficient training for reinforcement learning with adaptive behavior policy sharing"):
  with members:
    - "a generalized framework for population based training"
    - "a distributional perspective on reinforcement learning"
    - "population based training of neural networks"
    - "deep exploration via bootstrapped dqn"
    - "asynchronous methods for deep reinforcement learning"
    - "doubly robust off policy value evaluation for reinforcement learning"
    - "trust region policy optimization"
    - "playing atari with deep reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "a survey on string constraint solving"):
  with members:
    - "sound regular expression semantics for dynamic symbolic execution of javascript"
    - "what is decidable about string constraints with the replaceall function"
    - "undecidability of a theory of strings linear arithmetic over length and string number conversion"
    - "a solver for a theory of strings and bit vectors"
    - "minizinc with strings"
    - "regular combinators for string transformations"
    - "approximation of grammar based compression via recompression"
    - "recompression a simple and powerful technique for word equations"

Connected through hyperedge HE-4 (induced by source paper "constraint solving with deep learning for symbolic execution"):
  with members:
    - "deep learning using rectified linear units relu"
    - "enhancing reuse of constraint solutions to improve symbolic execution"
    - "deep residual learning for image recognition"
    - "ucf101 a dataset of 101 human actions classes from videos in the wild"
    - "satzilla portfolio based algorithm selection for sat"

Connected through hyperedge HE-5 (induced by source paper "neural large neighborhood search for the capacitated vehicle routing problem"):
  with members:
    - "an efficient graph convolutional network technique for the travelling salesman problem"
    - "machine learning for combinatorial optimization a methodological tour d horizon"
    - "learning the multiple traveling salesmen problem with permutation invariant pooling networks"
    - "residual gated graph convnets"
    - "deep learning assisted heuristic tree search for the container pre marshalling problem"
    - "learning combinatorial optimization algorithms over graphs"
    - "neural combinatorial optimization with reinforcement learning"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "evolutionary n level hypergraph partitioning with adaptive coarsening"):
  with members:
    - "memetic multilevel hypergraph partitioning"
    - "k way hypergraph partitioning via n level recursive bisection"
    - "parallel graph partitioning for complex networks"
    - "distributed evolutionary graph partitioning"
    - "n level graph partitioning"
    - "satzilla portfolio based algorithm selection for sat"

Connected through hyperedge HE-7 (induced by source paper "automatic algorithm selection in multi agent pathfinding"):
  with members:
    - "multi agent path finding with deadlines preliminary results"
    - "matconvnet convolutional neural networks for matlab"
    - "planning optimal paths for multiple robots on graphs"
    - "mapp a scalable multi agent path planning algorithm with tractability and completeness guarantees"

Connected through hyperedge HE-8 (induced by source paper "auto model utilizing research papers and hpo techniques to deal with the cash problem"):
  with members:
    - "the algorithm selection competitions 2015 and 2017"
    - "linear classifier design under heteroscedasticity in linear discriminant analysis"
    - "auto weka combined selection and hyperparameter optimization of classification algorithms"

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.AI(Artificial Intelligence)