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: "non asymptotic calibration and resolution"
  Abstract: We analyze a new algorithm for probability forecasting of binary observations on the basis of the available data, without making any assumptions about the way the observations are generated. The algorithm is shown to be well calibrated and to have good resolution for long enough sequences of observations and for a suitable choice of its parameter, a kernel on the Cartesian product of the forecast space $[0,1]$ and the data space. Our main results are non-asymptotic: we establish explicit inequalities, shown to be tight, for the performance of the algorithm.

Connected through hyperedge HE-1 (induced by source paper "calibration and internal no regret with partial monitoring"):
  with members:
    - "approachability of convex sets in games with partial monitoring"

Connected through hyperedge HE-2 (induced by source paper "leading strategies in competitive on line prediction"):
  with members:
    - "relative loss bounds for on line density estimation with the exponential family of distributions"
    - "competing with stationary prediction strategies"
    - "competing with markov prediction strategies"
    - "predictions as statements and decisions"
    - "competitive on line learning with a convex loss function"
    - "competing with wild prediction rules"
    - "on line regression competitive with reproducing kernel hilbert spaces"
    - "defensive forecasting for linear protocols"

Connected through hyperedge HE-3 (induced by source paper "metric entropy in competitive on line prediction"):
  with members:
    - "on line prediction with kernels and the complexity approximation principle"
    - "competing with stationary prediction strategies"
    - "leading strategies in competitive on line prediction"
    - "competing with markov prediction strategies"
    - "predictions as statements and decisions"
    - "competing with wild prediction rules"
    - "on line regression competitive with reproducing kernel hilbert spaces"

Connected through hyperedge HE-4 (induced by source paper "predictions as statements and decisions"):
  with members:
    - "competitive on line learning with a convex loss function"
    - "uniform test of algorithmic randomness over a general space"

Connected through hyperedge HE-5 (induced by source paper "on line regression competitive with reproducing kernel hilbert spaces"):
  with members:
    - "relative loss bounds for on line density estimation with the exponential family of distributions"
    - "on line prediction with kernels and the complexity approximation principle"
    - "competitive on line learning with a convex loss function"
    - "uniform test of algorithmic randomness over a general space"

Connected through hyperedge HE-6 (induced by source paper "competitive on line learning with a convex loss function"):
  with members:
    - "defensive forecasting for linear protocols"

Connected through hyperedge HE-7 (induced by source paper "defensive forecasting for linear protocols"):
  with members:
    - "competitive on line learning with a convex loss function"

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.LG(Machine Learning)