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: "contextual bandits with latent confounders an nmf approach"
  Abstract: Motivated by online recommendation and advertising systems, we consider a causal model for stochastic contextual bandits with a latent low-dimensional confounder. In our model, there are $L$ observed contexts and $K$ arms of the bandit. The observed context influences the reward obtained through a latent confounder variable with cardinality $m$ ($m \ll L,K$). The arm choice and the latent confounder causally determines the reward while the observed context is correlated with the confounder. Under this model, the $L \times K$ mean reward matrix $\mathbf{U}$ (for each context in $[L]$ and each arm in $[K]$) factorizes into non-negative factors $\mathbf{A}$ ($L \times m$) and $\mathbf{W}$ ($m \times K$). This insight enables us to propose an $\epsilon$-greedy NMF-Bandit algorithm that designs a sequence of interventions (selecting specific arms), that achieves a balance between learning this low-dimensional structure and selecting the best arm to minimize regret. Our algorithm achieves a regret of $\mathcal{O}\left(L\mathrm{poly}(m, \log K) \log T \right)$ at time $T$, as compared to $\mathcal{O}(LK\log T)$ for conventional contextual bandits, assuming a constant gap between the best arm and the rest for each context. These guarantees are obtained under mild sufficiency conditions on the factors that are weaker versions of the well-known Statistical RIP condition. We further propose a class of generative models that satisfy our sufficient conditions, and derive a lower bound of $\mathcal{O}\left(Km\log T\right)$. These are the first regret guarantees for online matrix completion with bandit feedback, when the rank is greater than one. We further compare the performance of our algorithm with the state of the art, on synthetic and real world data-sets.

Connected through hyperedge HE-1 (induced by source paper "warm starting bandits with side information from confounded data"):
  with members:
    - "combining offline causal inference and online bandit learning for data driven decisions"
    - "graphical models for bandit problems"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "from bandits to experts on the value of side observations"
    - "a contextual bandit approach to personalized news article recommendation"
    - "multi armed bandits in metric spaces"

Connected through hyperedge HE-2 (induced by source paper "bilinear bandits with low rank structure"):
  with members:
    - "factored bandits"
    - "horde of bandits using gaussian markov random fields"
    - "bernoulli rank 1 bandits for click feedback"
    - "stochastic low rank bandits"
    - "stochastic rank 1 bandits"
    - "on context dependent clustering of bandits"
    - "low rank bandits with latent mixtures"
    - "collaborative filtering bandits"

Connected through hyperedge HE-3 (induced by source paper "edge computing in the dark leveraging contextual combinatorial bandit and coded computing"):
  with members:
    - "timely throughput optimal coded computing over cloud networks"
    - "combinatorial sleeping bandits with fairness constraints"
    - "hierarchical coded computation"
    - "speeding up distributed machine learning using codes"
    - "differentially private contextual linear bandits"
    - "communication aware scheduling of serial tasks for dispersed computing"
    - "lagrange coded computing optimal design for resiliency security and privacy"
    - "coded computation over heterogeneous clusters"

Connected through hyperedge HE-4 (induced by source paper "off policy evaluation in partially observable environments"):
  with members:
    - "counterfactual off policy evaluation with gumbel max structural causal models"
    - "double reinforcement learning for efficient off policy evaluation in markov decision processes"
    - "batch policy learning under constraints"
    - "breaking the curse of horizon infinite horizon off policy estimation"
    - "theoretical impediments to machine learning with seven sparks from the causal revolution"
    - "evaluating reinforcement learning algorithms in observational health settings"
    - "deconfounding reinforcement learning in observational settings"
    - "linguistically regularized lstms for sentiment classification"

Connected through hyperedge HE-5 (induced by source paper "causal simulations for uplift modeling"):
  with members:
    - "contextual multi armed bandits for causal marketing"
    - "a contextual bandit approach to personalized news article recommendation"

Connected through hyperedge HE-6 (induced by source paper "towards optimal and efficient best arm identification in linear bandits"):
  with members:
    - "best arm identification in linear bandits"

Connected through hyperedge HE-7 (induced by source paper "deconfounding reinforcement learning in observational settings"):
  with members:
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "world models"
    - "woulda coulda shoulda counterfactually guided policy search"
    - "evaluating reinforcement learning algorithms in observational health settings"
    - "deep counterfactual networks with propensity dropout"
    - "continuous state space models for optimal sepsis treatment a deep reinforcement learning approach"
    - "a reinforcement learning approach to weaning of mechanical ventilation in intensive care units"
    - "deep reinforcement learning for sepsis treatment"

Connected through hyperedge HE-8 (induced by source paper "low rank bandit methods for high dimensional dynamic pricing"):
  with members:
    - "perishability of data dynamic pricing under varying coefficient models"
    - "online learning with low rank experts"
    - "kernel based methods for bandit convex optimization"
    - "low rank bandits with latent mixtures"
    - "on graduated optimization for stochastic non convex problems"
    - "the best of both worlds stochastic and adversarial bandits"
    - "online convex optimization in the bandit setting gradient descent without a gradient"

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)