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: "mixing strategies in data compression"
  Abstract: We propose geometric weighting as a novel method to combine multiple models in data compression. Our results reveal the rationale behind PAQ-weighting and generalize it to a non-binary alphabet. Based on a similar technique we present a new, generic linear mixture technique. All novel mixture techniques rely on given weight vectors. We consider the problem of finding optimal weights and show that the weight optimization leads to a strictly convex (and thus, good-natured) optimization problem. Finally, an experimental evaluation compares the two presented mixture techniques for a binary alphabet. The results indicate that geometric weighting is superior to linear weighting.

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 "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"
    - "linear and geometric mixtures analysis"
    - "context tree switching"

Connected through hyperedge HE-3 (induced by source paper "packet level network compression realization and scaling of the network wide benefits"):
  with members:
    - "a universal parallel two pass mdl context tree compression algorithm"
    - "memory assisted universal compression of network flows"
    - "results on the fundamental gain of memory assisted universal source coding"
    - "results on the redundancy of universal compression for finite length sequences"
    - "on the network wide gain of memory assisted source coding"
    - "sustaining the internet with hyperbolic mapping"

Connected through hyperedge HE-4 (induced by source paper "linear and geometric mixtures analysis"):
  with members:
    - "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)