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: "from stochastic mixability to fast rates"
  Abstract: Empirical risk minimization (ERM) is a fundamental learning rule for statistical learning problems where the data is generated according to some unknown distribution $\mathsf{P}$ and returns a hypothesis $f$ chosen from a fixed class $\mathcal{F}$ with small loss $\ell$. In the parametric setting, depending upon $(\ell, \mathcal{F},\mathsf{P})$ ERM can have slow $(1/\sqrt{n})$ or fast $(1/n)$ rates of convergence of the excess risk as a function of the sample size $n$. There exist several results that give sufficient conditions for fast rates in terms of joint properties of $\ell$, $\mathcal{F}$, and $\mathsf{P}$, such as the margin condition and the Bernstein condition. In the non-statistical prediction with expert advice setting, there is an analogous slow and fast rate phenomenon, and it is entirely characterized in terms of the mixability of the loss $\ell$ (there being no role there for $\mathcal{F}$ or $\mathsf{P}$). The notion of stochastic mixability builds a bridge between these two models of learning, reducing to classical mixability in a special case. The present paper presents a direct proof of fast rates for ERM in terms of stochastic mixability of $(\ell,\mathcal{F}, \mathsf{P})$, and in so doing provides new insight into the fast-rates phenomenon. The proof exploits an old result of Kemperman on the solution to the general moment problem. We also show a partial converse that suggests a characterization of fast rates for ERM in terms of stochastic mixability is possible.

Connected through hyperedge HE-1 (induced by source paper "pac bayes un expected bernstein inequality"):
  with members:
    - "computing nonvacuous generalization bounds for deep stochastic neural networks with many more parameters than training data"
    - "fast rates with high probability in exp concave statistical learning"
    - "fast rates for general unbounded loss functions from erm to generalized bayes"
    - "combining adversarial guarantees and stochastic fast rates in online learning"
    - "fast rates in statistical and online learning"
    - "a note on the pac bayesian theorem"

Connected through hyperedge HE-2 (induced by source paper "learning with non convex truncated losses by sgd"):
  with members:
    - "empirical risk minimization for stochastic convex optimization o 1 n and o 1 n 2 type of risk bounds"
    - "fast rates for general unbounded loss functions from erm to generalized bayes"
    - "learning without concentration"
    - "relative deviation learning bounds and generalization with unbounded loss functions"
    - "loss minimization and parameter estimation with heavy tails"

Connected through hyperedge HE-3 (induced by source paper "fast rates for general unbounded loss functions from erm to generalized bayes"):
  with members:
    - "a tight excess risk bound via a unified pac bayesian rademacher shtarkov mdl complexity"
    - "combining adversarial guarantees and stochastic fast rates in online learning"
    - "fast rates in statistical and online learning"
    - "learning without concentration"
    - "renyi divergence and kullback leibler divergence"
    - "minimax analysis of active learning"
    - "follow the leader if you can hedge if you must"
    - "relative deviation learning bounds and generalization with unbounded loss functions"

Connected through hyperedge HE-4 (induced by source paper "combining adversarial guarantees and stochastic fast rates in online learning"):
  with members:
    - "metagrad multiple learning rates in online learning"
    - "fast rates in statistical and online learning"
    - "second order quantile methods for experts and combinatorial games"
    - "achieving all with no parameters adaptive normalhedge"
    - "follow the leader if you can hedge if you must"
    - "adaptive bound optimization for online convex optimization"

Connected through hyperedge HE-5 (induced by source paper "from exp concavity to variance control high probability o 1 n rates and high probability online to batch conversion"):
  with members:
    - "average stability is invariant to data preconditioning implications to exp concave empirical risk minimization"
    - "fast rates in statistical and online learning"
    - "excess risk bounds for exponentially concave losses"

Connected through hyperedge HE-6 (induced by source paper "fast rates with unbounded losses"):
  with members:
    - "learning without concentration"
    - "renyi divergence and kullback leibler divergence"

Connected through hyperedge HE-7 (induced by source paper "fast rates in statistical and online learning"):
  with members:
    - "exp concavity of proper composite losses"
    - "learning without concentration"
    - "follow the leader if you can hedge if you must"
    - "supermartingales in prediction with expert advice"
    - "prediction with expert advice for the brier game"

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)