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: "sparse adaptive dirichlet multinomial like processes"
  Abstract: Online estimation and modelling of i.i.d. data for short sequences over large or complex "alphabets" is a ubiquitous (sub)problem in machine learning, information theory, data compression, statistical language processing, and document analysis. The Dirichlet-Multinomial distribution (also called Polya urn scheme) and extensions thereof are widely applied for online i.i.d. estimation. Good a-priori choices for the parameters in this regime are difficult to obtain though. I derive an optimal adaptive choice for the main parameter via tight, data-dependent redundancy bounds for a related model. The 1-line recommendation is to set the 'total mass' = 'precision' = 'concentration' parameter to m/2ln[(n+1)/m], where n is the (past) sample size and m the number of different symbols observed (so far). The resulting estimator (i) is simple, (ii) online, (iii) fast, (iv) performs well for all m, small, middle and large, (v) is independent of the base alphabet size, (vi) non-occurring symbols induce no redundancy, (vii) the constant sequence has constant redundancy, (viii) symbols that appear only finitely often have bounded/constant contribution to the redundancy, (ix) is competitive with (slow) Bayesian mixing over all sub-alphabets.

Connected through hyperedge HE-1 (induced by source paper "soft bayes prod for mixtures of experts with log loss"):
  with members:
    - "introduction to online convex optimization"
    - "efficient second order online learning by sketching"
    - "context tree switching"

Connected through hyperedge HE-2 (induced by source paper "unifying count based exploration and intrinsic motivation"):
  with members:
    - "asynchronous methods for deep reinforcement learning"
    - "thompson sampling is asymptotically optimal in general environments"
    - "vime variational information maximizing exploration"
    - "pixel recurrent neural networks"
    - "state of the art control of atari games using shallow reinforcement learning"
    - "prioritized experience replay"
    - "increasing the action gap new operators for reinforcement learning"
    - "action conditional video prediction using deep networks in atari games"

Connected through hyperedge HE-3 (induced by source paper "compress and control"):
  with members:
    - "the arcade learning environment an evaluation platform for general agents"
    - "playing atari with deep reinforcement learning"
    - "learning is planning near bayes optimal reinforcement learning via monte carlo tree search"
    - "efficient bayes adaptive reinforcement learning using sample based search"
    - "partition tree weighting"
    - "efficient tracking of large classes of experts"
    - "reinforcement learning via aixi approximation"
    - "a monte carlo aixi approximation"

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)