human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "procrastinating with confidence near optimal anytime adaptive algorithm configuration"):
  Abstract: Algorithm configuration methods optimize the performance of a parameterized heuristic algorithm on a given distribution of problem instances. Recent work introduced an algorithm configuration procedure (``Structured Procrastination'') that provably achieves near optimal performance with high probability and with nearly minimal runtime in the worst case. It also offers an \emph{anytime} property: it keeps tightening its optimality guarantees the longer it is run. Unfortunately, Structured Procrastination is not \emph{adaptive} to characteristics of the parameterized algorithm: it treats every input like the worst case. Follow-up work (``Leaps and Bounds'') achieves adaptivity but trades away the anytime property. This paper introduces a new algorithm, ``Structured Procrastination with Confidence'', that preserves the near-optimality and anytime properties of Structured Procrastination while adding adaptivity. In particular, the new algorithm will perform dramatically faster in settings where many algorithm configurations perform poorly. We show empirically both that such settings arise frequently in practice and that the anytime property is useful for finding good configurations quickly.

Includes members:
  Member "learning to branch":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "lossless compression of deep neural networks")
  Member "leapsandbounds a method for approximately optimal algorithm configuration":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "benchmark and survey of automated machine learning frameworks")
  Member "dispersion for data driven algorithm design online learning and private optimization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning piecewise lipschitz functions in changing environments")
  Member "efficient hyperparameter optimization and infinitely many armed bandits":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "hyperband a novel bandit based approach to hyperparameter optimization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "learning theoretic foundations of algorithm configuration for combinatorial partitioning problems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning piecewise lipschitz functions in changing environments")
  Member "a pac approach to application specific algorithm selection":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning complexity of simulated annealing")
  Member "bandits with knapsacks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "exploration exploitation in constrained mdps")

Question: Please tell me which class the source paper 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)