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: "online distributed sensor selection"
  Abstract: A key problem in sensor networks is to decide which sensors to query when, in order to obtain the most useful information (e.g., for performing accurate prediction), subject to constraints (e.g., on power and bandwidth). In many applications the utility function is not known a priori, must be learned from data, and can even change over time. Furthermore for large sensor networks solving a centralized optimization problem to select sensors is not feasible, and thus we seek a fully distributed solution. In this paper, we present Distributed Online Greedy (DOG), an efficient, distributed algorithm for repeatedly selecting sensors online, only receiving feedback about the utility of the selected sensors. We prove very strong theoretical no-regret guarantees that apply whenever the (unknown) utility function satisfies a natural diminishing returns property called submodularity. Our algorithm has extremely low communication requirements, and scales well to large sensor deployments. We extend DOG to allow observation-dependent sensor selection. We empirically demonstrate the effectiveness of our algorithm on several real-world sensing tasks.

Connected through hyperedge HE-1 (induced by source paper "periodic bandits and wireless network selection"):
  with members:
    - "cooperation speeds surfing use co bandit"
    - "congestion aware distributed network selection for integrated cellular and wi fi networks"
    - "shrewd selection speeds surfing use smart exp3"
    - "efficient algorithms for adversarial contextual learning"
    - "multi user lax communications a multi armed bandit approach"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "performance and convergence of multi user online learning"

Connected through hyperedge HE-2 (induced by source paper "a distributed learning dynamics in social groups"):
  with members:
    - "ant inspired density estimation via random walks"
    - "human collective intelligence as distributed bayesian inference"
    - "irls and slime mold equivalence and convergence"
    - "distributed house hunting in ant colonies"
    - "stabilizing consensus with many opinions"
    - "distributed algorithms for learning and cognitive medium access with logarithmic regret"
    - "decentralized online learning algorithms for opportunistic spectrum access"
    - "electrical flows laplacian systems and faster approximation of maximum flow in undirected graphs"

Connected through hyperedge HE-3 (induced by source paper "insense incoherent sensor selection for sparse signals"):
  with members:
    - "near optimal sensor placement for linear inverse problems"
    - "projection onto the probability simplex an efficient algorithm with a simple proof and an application"
    - "sparsity promoting sensor selection for non linear measurement models"
    - "exact recovery conditions for sparse representations with partial support information"
    - "deterministic construction of binary bipolar and ternary compressed sensing matrices"
    - "message passing algorithms for compressed sensing"

Connected through hyperedge HE-4 (induced by source paper "shrewd selection speeds surfing use smart exp3"):
  with members:
    - "congestion aware distributed network selection for integrated cellular and wi fi networks"
    - "performance and convergence of multi user online learning"

Connected through hyperedge HE-5 (induced by source paper "microphone subset selection for mvdr beamformer based noise reduction"):
  with members:
    - "sparsity promoting sensor selection for non linear measurement models"

Connected through hyperedge HE-6 (induced by source paper "submodular optimization under noise"):
  with members:
    - "when are welfare guarantees robust"
    - "noisy submodular maximization via adaptive sampling with applications to crowdsourced image collection summarization"
    - "tight bounds on low degree spectral concentration of submodular and xos functions"
    - "budget feasible mechanisms for experimental design"
    - "optimal bounds on approximation of submodular and xos functions by juntas"
    - "representation approximation and learning of submodular functions using low rank decision trees"
    - "learning coverage functions and private release of marginals"
    - "the computational complexity of truthfulness in combinatorial auctions"

Connected through hyperedge HE-7 (induced by source paper "wiggins detecting valuable information in dynamic networks using limited resources"):
  with members:
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "irie scalable and robust influence maximization in social networks"

Connected through hyperedge HE-8 (induced by source paper "active learning for autonomous intelligent agents exploration curiosity and interaction"):
  with members:
    - "multi class generalized binary search for active inverse reinforcement learning"
    - "active learning of inverse models with intrinsically motivated goal exploration in robots"
    - "selecting the state representation in reinforcement learning"
    - "local utility elicitation in gai models"
    - "gaussian processes for sample efficient reinforcement learning with rmax like exploration"
    - "variance based rewards for approximate bayesian reinforcement learning"
    - "near optimal nonmyopic value of information in graphical models"
    - "safe exploration in markov decision processes"

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)