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: "dash dynamic approach for switching heuristics"
  Abstract: Complete tree search is a highly effective method for tackling MIP problems, and over the years, a plethora of branching heuristics have been introduced to further refine the technique for varying problems. Recently, portfolio algorithms have taken the process a step further, trying to predict the best heuristic for each instance at hand. However, the motivation behind algorithm selection can be taken further still, and used to dynamically choose the most appropriate algorithm for each encountered subproblem. In this paper we identify a feature space that captures both the evolution of the problem in the branching tree and the similarity among subproblems of instances from the same MIP models. We show how to exploit these features to decide the best time to switch the branching heuristic and then show how such a system can be trained efficiently. Experiments on a highly heterogeneous collection of MIP instances show significant gains over the pure algorithm selection approach that for a given instance uses only a single heuristic throughout the search.

Connected through hyperedge HE-1 (induced by source paper "learning to optimize computational resources frugal training with generalization guarantees"):
  with members:
    - "semi bandit optimization in the dispersed setting"
    - "learning to prune speeding up repeated computations"
    - "procrastinating with confidence near optimal anytime adaptive algorithm configuration"
    - "learning to link"
    - "learning to branch"
    - "learning robust search strategies using a bandit based approach"
    - "leapsandbounds a method for approximately optimal algorithm configuration"
    - "dispersion for data driven algorithm design online learning and private optimization"

Connected through hyperedge HE-2 (induced by source paper "exact combinatorial optimization with graph convolutional neural networks"):
  with members:
    - "machine learning for combinatorial optimization a methodological tour d horizon"
    - "learning heuristics for automated reasoning through deep reinforcement learning"
    - "how powerful are graph neural networks"
    - "neural message passing for quantum chemistry"
    - "adam a method for stochastic optimization"

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 "methods for off line on line optimization under uncertainty"):
  with members:
    - "deep neural networks as 0 1 mixed integer linear programs a feasibility study"
    - "deep learning assisted heuristic tree search for the container pre marshalling problem"
    - "task based end to end model learning in stochastic optimization"
    - "reluplex an efficient smt solver for verifying deep neural networks"
    - "satzilla portfolio based algorithm selection for sat"

Connected through hyperedge HE-5 (induced by source paper "boosting binary optimization via binary classification a case study of job shop scheduling"):
  with members:
    - "learning to learn by gradient descent by gradient descent"
    - "aslib a benchmark library for algorithm selection"
    - "new techniques for algorithm portfolio design"

Connected through hyperedge HE-6 (induced by source paper "learning to branch"):
  with members:
    - "learning robust search strategies using a bandit based approach"

Connected through hyperedge HE-7 (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"
    - "markov chain methods for the bipartite boolean quadratic programming 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"

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)