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: "lazier than lazy greedy"
  Abstract: Is it possible to maximize a monotone submodular function faster than the widely used lazy greedy algorithm (also known as accelerated greedy), both in theory and practice? In this paper, we develop the first linear-time algorithm for maximizing a general monotone submodular function subject to a cardinality constraint. We show that our randomized algorithm, STOCHASTIC-GREEDY, can achieve a $(1-1/e-\varepsilon)$ approximation guarantee, in expectation, to the optimum solution in time linear in the size of the data and independent of the cardinality constraint. We empirically demonstrate the effectiveness of our algorithm on submodular functions arising in data summarization, including training large-scale kernel methods, exemplar-based clustering, and sensor placement. We observe that STOCHASTIC-GREEDY practically achieves the same utility value as lazy greedy but runs much faster. More surprisingly, we observe that in many practical scenarios STOCHASTIC-GREEDY does not evaluate the whole fraction of data points even once and still achieves indistinguishable results compared to lazy greedy.

Connected through hyperedge HE-1 (induced by source paper "good feature matching towards accurate robust vo vslam with low latency"):
  with members:
    - "low latency visual slam with appearance enhanced local map building"
    - "good feature selection for least squares pose optimization in vo vslam"
    - "navigating the landscape for real time localization and mapping for robotics and virtual and augmented reality"
    - "the open vision computer an integrated sensing and compute system for mobile robots"
    - "direct sparse odometry with rolling shutter"
    - "futuremapping the computational structure of spatial ai systems"
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "the tum vi benchmark for evaluating visual inertial odometry"

Connected through hyperedge HE-2 (induced by source paper "on the unreasonable effectiveness of the greedy algorithm greedy adapts to sharpness"):
  with members:
    - "guess free maximization of submodular and linear sums"
    - "stability and recovery for independence systems"
    - "constrained submodular maximization beyond 1 e"
    - "stability of influence maximization"
    - "deterministic algorithms for submodular maximization problems"
    - "computational feasibility of clustering under clusterability assumptions"
    - "curvature and optimal algorithms for learning and minimizing submodular functions"
    - "optimal approximation for submodular and supermodular optimization with bounded curvature"

Connected through hyperedge HE-3 (induced by source paper "crowdsourced collective entity resolution with relational match propagation"):
  with members:
    - "minoaner schema agnostic non iterative massively parallel resolution of web entities"
    - "principled graph matching algorithms for integrating multiple data sources"
    - "leveraging transitive relations for crowdsourced joins"
    - "sigma simple greedy matching for aligning large knowledge bases"
    - "crowder crowdsourcing entity resolution"
    - "large scale collective entity matching"
    - "paris probabilistic alignment of relations instances and schema"

Connected through hyperedge HE-4 (induced by source paper "progressive stochastic greedy sparse reconstruction and support selection"):
  with members:
    - "scalable sparse subspace clustering by orthogonal matching pursuit"
    - "greedy column subset selection for large scale data sets"
    - "a remark on the restricted isometry property in orthogonal matching pursuit"
    - "optimal column based low rank matrix reconstruction"
    - "near optimal column based matrix reconstruction"
    - "sparse recovery with orthogonal matching pursuit under rip"
    - "subspace pursuit for compressive sensing signal reconstruction"

Connected through hyperedge HE-5 (induced by source paper "ranked list loss for deep metric learning"):
  with members:
    - "deep randomized ensembles for metric learning"
    - "deep metric learning by online soft mining and class aware attention"
    - "deep metric learning with hierarchical triplet loss"
    - "attention based ensemble for deep metric learning"
    - "in defense of the triplet loss for person re identification"
    - "deep metric learning with angular loss"
    - "no fuss distance metric learning using proxies"
    - "smart mining for deep metric learning"

Connected through hyperedge HE-6 (induced by source paper "submodular streaming in all its glory tight approximation minimum memory and low adaptive complexity"):
  with members:
    - "an exponential speedup in parallel running time for submodular maximization without loss in approximation"
    - "data summarization at scale a two stage submodular approach"
    - "submodular maximization with optimal approximation adaptivity and query complexity"
    - "unconstrained submodular maximization with constant adaptive complexity"
    - "do less get more streaming submodular maximization with subsampling"
    - "parallelizing greedy for submodular set function maximization in matroids and beyond"
    - "non monotone submodular maximization in exponentially fewer iterations"
    - "beyond 1 2 approximation for submodular maximization on massive data streams"

Connected through hyperedge HE-7 (induced by source paper "seeding with costly network information"):
  with members:
    - "beyond worst case in approximability of nonsubmodular influence maximization"
    - "metric sublinear algorithms via linear sampling"
    - "the importance of communities for learning to influence"
    - "stochastic submodular maximization the case of coverage functions"
    - "online influence maximization under independent cascade model with semi bandit feedback"
    - "robust influence maximization"
    - "almost optimal streaming algorithms for coverage problems"
    - "online influence maximization"

Connected through hyperedge HE-8 (induced by source paper "guarantees of stochastic greedy algorithms for non monotone submodular maximization with cardinality constraint"):
  with members:
    - "interlaced greedy algorithm for maximization of submodular functions in nearly linear time"
    - "a memoization framework for scaling submodular optimization to large scale problems"
    - "non monotone submodular maximization with nearly optimal adaptivity and query complexity"
    - "submodular maximization beyond non negativity guarantees fast algorithms and applications"
    - "stochastic greedy closing the optimality gap in exact weak submodular maximization"
    - "non monotone submodular maximization in exponentially fewer iterations"
    - "a randomized greedy algorithm for near optimal sensor scheduling in large scale sensor networks"
    - "pmlb a large benchmark suite for machine learning evaluation and comparison"

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)