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: "learning causal graphs with small interventions"
  Abstract: We consider the problem of learning causal networks with interventions, when each intervention is limited in size under Pearl's Structural Equation Model with independent errors (SEM-IE). The objective is to minimize the number of experiments to discover the causal directions of all the edges in a causal graph. Previous work has focused on the use of separating systems for complete graphs for this task. We prove that any deterministic adaptive algorithm needs to be a separating system in order to learn complete graphs in the worst case. In addition, we present a novel separating system construction, whose size is close to optimal and is arguably simpler than previous work in combinatorics. We also develop a novel information theoretic lower bound on the number of interventions that applies in full generality, including for randomized adaptive learning algorithms. #R##N#For general chordal graphs, we derive worst case lower bounds on the number of interventions. Building on observations about induced trees, we give a new deterministic adaptive algorithm to learn directions on any chordal skeleton completely. In the worst case, our achievable scheme is an $\alpha$-approximation algorithm where $\alpha$ is the independence number of the graph. We also show that there exist graph classes for which the sufficient number of experiments is close to the lower bound. In the other extreme, there are graph classes for which the required number of experiments is multiplicatively $\alpha$ away from our lower bound. #R##N#In simulations, our algorithm almost always performs very close to the lower bound, while the approach based on separating systems for complete graphs is significantly worse for random chordal graphs.

Connected through hyperedge HE-1 (induced by source paper "hypothesis driven skill discovery for hierarchical deep reinforcement learning"):
  with members:
    - "go explore a new approach for hard exploration problems"
    - "monet unsupervised scene decomposition and representation"
    - "model based reinforcement learning for atari"
    - "a review of robot learning for manipulation challenges representations and algorithms"
    - "multi object representation learning with iterative variational inference"
    - "exploration by random network distillation"
    - "learning real world robot policies by dreaming"
    - "relational deep reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "causal confusion in imitation learning"):
  with members:
    - "exploring the limitations of behavior cloning for autonomous driving"
    - "monocular plan view networks for autonomous driving"
    - "a fast pc algorithm for high dimensional causal discovery with multi core pcs"
    - "causal inference via kernel deviance measures"
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "chauffeurnet learning to drive by imitating the best and synthesizing the worst"
    - "isolating sources of disentanglement in variational autoencoders"

Connected through hyperedge HE-3 (induced by source paper "interventional experiment design for causal structure learning"):
  with members:
    - "experimental design for cost aware learning of causal graphs"
    - "counting and uniform sampling from markov equivalent dags"
    - "counting and sampling from markov equivalent dags using clique trees"
    - "budgeted experiment design for causal structure learning"
    - "cost optimal learning of causal graphs"
    - "lazier than lazy greedy"
    - "an algorithm for deciding if a set of observed independencies has a causal explanation"
    - "learning bayesian networks the combination of knowledge and statistical data"

Connected through hyperedge HE-4 (induced by source paper "computationally and statistically efficient learning of causal bayes nets using path queries"):
  with members:
    - "reconstructing a bounded degree directed tree using path queries"
    - "powers of tensors and fast matrix multiplication"
    - "sparsityboost a new scoring function for learning bayesian network structure"
    - "on the number of experiments sufficient and in the worst case necessary to identify all causal relations among n variables"

Connected through hyperedge HE-5 (induced by source paper "innovation representation of stochastic processes with application to causal inference"):
  with members:
    - "on the universality of the logistic loss function"
    - "lossless and lossy compression of random forests"
    - "bregman divergence bounds and the universality of the logarithmic loss"
    - "entropic causal inference"
    - "large alphabet source coding using independent component analysis"
    - "generalized independent component analysis over finite alphabets"
    - "distinguishing cause from effect using observational data methods and benchmarks"
    - "on the hardness of entropy minimization and related problems"

Connected through hyperedge HE-6 (induced by source paper "budgeted experiment design for causal structure learning"):
  with members:
    - "counting and uniform sampling from markov equivalent dags"
    - "learning causal structures using regression invariance"
    - "cost optimal learning of causal graphs"
    - "causal network inference by optimal causation entropy"
    - "an algorithm for deciding if a set of observed independencies has a causal explanation"
    - "causal discovery from a mixture of experimental and observational data"
    - "directed information graphs"
    - "bayesian model averaging using the k best bayesian network structures"

Connected through hyperedge HE-7 (induced by source paper "learning causal structures using regression invariance"):
  with members:
    - "optimal experiment design for causal discovery from fixed number of experiments"
    - "interaction information for causal inference the case of directed triangle"
    - "discovery and visualization of nonstationary causal models"
    - "an algorithm for deciding if a set of observed independencies has a causal explanation"
    - "causal discovery from changes"
    - "inferring deterministic causal relations"
    - "on the number of experiments sufficient and in the worst case necessary to identify all causal relations among n variables"

Connected through hyperedge HE-8 (induced by source paper "optimal experiment design for causal discovery from fixed number of experiments"):
  with members:
    - "distinguishing cause from effect using observational data methods and benchmarks"
    - "causal discovery from a mixture of experimental and observational data"
    - "on the number of experiments sufficient and in the worst case necessary to identify all causal relations among n variables"

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.AI(Artificial Intelligence)