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: "a comprehensive approach to universal piecewise nonlinear regression based on trees"
  Abstract: In this paper, we investigate adaptive nonlinear regression and introduce tree based piecewise linear regression algorithms that are highly efficient and provide significantly improved performance with guaranteed upper bounds in an individual sequence manner. We use a tree notion in order to partition the space of regressors in a nested structure. The introduced algorithms adapt not only their regression functions but also the complete tree structure while achieving the performance of the "best" linear mixture of a doubly exponential number of partitions, with a computational complexity only polynomial in the number of nodes of the tree. While constructing these algorithms, we also avoid using any artificial "weighting" of models (with highly data dependent parameters) and, instead, directly minimize the final regression error, which is the ultimate performance goal. The introduced methods are generic such that they can readily incorporate different tree construction methods such as random trees in their framework and can use different regressor or partitioning functions as demonstrated in the paper.

Connected through hyperedge HE-1 (induced by source paper "highly efficient hierarchical online nonlinear regression using second order methods"):
  with members:
    - "signal processing in large systems a new paradigm"

Connected through hyperedge HE-2 (induced by source paper "an asymptotically optimal contextual bandit algorithm using hierarchical structures"):
  with members:
    - "randomized prediction games for adversarial machine learning"
    - "efficient algorithms for adversarial contextual learning"
    - "distributed online learning in social recommender systems"
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "contextual bandit learning with predictable rewards"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "unbiased offline evaluation of contextual bandit based news article recommendation algorithms"
    - "efficient optimal learning for contextual bandits"

Connected through hyperedge HE-3 (induced by source paper "adaptive and efficient nonlinear channel equalization for underwater acoustic communication"):
  with members:
    - "sequences with small correlation"

Connected through hyperedge HE-4 (induced by source paper "predicting nearly as well as the optimal twice differentiable regressor"):
  with members:
    - "sparse volterra and polynomial regression models recoverability and estimation"

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)