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: "linear and geometric mixtures analysis"
  Abstract: Linear and geometric mixtures are two methods to combine arbitrary models in data compression. Geometric mixtures generalize the empirically well-performing PAQ7 mixture. Both mixture schemes rely on weight vectors, which heavily determine their performance. Typically weight vectors are identified via Online Gradient Descent. In this work we show that one can obtain strong code length bounds for such a weight estimation scheme. These bounds hold for arbitrary input sequences. For this purpose we introduce the class of nice mixtures and analyze how Online Gradient Descent with a fixed step size combined with a nice mixture performs. These results translate to linear and geometric mixtures, which are nice, as we show. The results hold for PAQ7 mixtures as well, thus we provide the first theoretical analysis of PAQ7.

Connected through hyperedge HE-1 (induced by source paper "online learning in contextual bandits using gated linear networks"):
  with members:
    - "gated linear networks"
    - "parameter space noise for exploration"
    - "online learning with gated linear networks"
    - "a distributional perspective on reinforcement learning"
    - "deep exploration via bootstrapped dqn"
    - "exploration a study of count based exploration for deep reinforcement learning"
    - "unifying count based exploration and intrinsic motivation"
    - "compress and control"

Connected through hyperedge HE-2 (induced by source paper "gated linear networks"):
  with members:
    - "online learning with gated linear networks"
    - "pixel recurrent neural networks"
    - "exact solutions to the nonlinear dynamics of learning in deep linear neural networks"
    - "a machine learning perspective on predictive coding with paq"

Connected through hyperedge HE-3 (induced by source paper "online learning with gated linear networks"):
  with members:
    - "introduction to online convex optimization"
    - "deep online convex optimization with gated games"
    - "efficient second order online learning by sketching"
    - "an architecture for deep hierarchical generative models"
    - "compress and control"
    - "exact solutions to the nonlinear dynamics of learning in deep linear neural networks"
    - "mixing strategies in data compression"
    - "context tree switching"

Connected through hyperedge HE-4 (induced by source paper "online learning of k cnf boolean functions"):
  with members:
    - "partition tree weighting"
    - "context tree switching"

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.IT(Information Theory)