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: "distributable consistent multi object matching"
  Abstract: In this paper we propose an optimization-based framework to multiple object matching. The framework takes maps computed between pairs of objects as input, and outputs maps that are consistent among all pairs of objects. The central idea of our approach is to divide the input object collection into overlapping sub-collections and enforce map consistency among each sub-collection. This leads to a distributed formulation, which is scalable to large-scale datasets. We also present an equivalence condition between this decoupled scheme and the original scheme. Experiments on both synthetic and real-world datasets show that our framework is competitive against state-of-the-art multi-object matching techniques.

Connected through hyperedge HE-1 (induced by source paper "rgb d individual segmentation"):
  with members:
    - "lf net learning local features from images"
    - "srda generating instance segmentation annotation via scanning reasoning and domain adaptation"
    - "applying domain randomization to synthetic data for object category detection"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "neighbourhood consensus networks"
    - "semi parametric image synthesis"
    - "path aggregation network for instance segmentation"
    - "sniper efficient multi scale training"

Connected through hyperedge HE-2 (induced by source paper "probabilistic permutation synchronization using the riemannian structure of the birkhoff polytope"):
  with members:
    - "synchronisation of partial multi matchings via non negative factorisations"
    - "ppfnet global context aware local features for robust 3d point matching"
    - "bayesian pose graph optimization via bingham distributions and tempered geodesic mcmc"
    - "averaging stochastic gradient descent on riemannian manifolds"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "cad priors for accurate and flexible instance reconstruction"
    - "consistent multiple graph matching with multi layer random walks synchronization"
    - "multi image semantic matching by mining consistent features"

Connected through hyperedge HE-3 (induced by source paper "synchronisation of partial multi matchings via non negative factorisations"):
  with members:
    - "ds a flexible scalable and provably tight relaxation for matching problems"
    - "a constrained clustering based approach for matching a collection of feature sets"
    - "spectral synchronization of multiple views in se 3"
    - "efficient and non convex coordinate descent for symmetric nonnegative matrix factorization"
    - "multi graph matching via affinity optimization with graduated consistency regularization"
    - "a dual ascent framework for lagrangean decomposition of combinatorial problems"
    - "alternating direction graph matching"
    - "a solution for multi alignment by transformation synchronisation"

Connected through hyperedge HE-4 (induced by source paper "clear a consistent lifting embedding and alignment rectification algorithm for multi agent data association"):
  with members:
    - "synchronisation of partial multi matchings via non negative factorisations"
    - "all graphs lead to rome learning geometric and cycle consistent representations with graph convolutional networks"
    - "multi image semantic matching by mining consistent features"
    - "a constrained clustering based approach for matching a collection of feature sets"
    - "multi graph matching via affinity optimization with graduated consistency regularization"
    - "netvlad cnn architecture for weakly supervised place recognition"
    - "multi image matching via fast alternating minimization"
    - "near optimal joint object matching via convex relaxation"

Connected through hyperedge HE-5 (induced by source paper "distributed and consistent multi image feature matching via quickmatch"):
  with members:
    - "ps dbscan an efficient parallel dbscan algorithm based on platform of ai pai"
    - "billion scale similarity search with gpus"
    - "anchornet a weakly supervised network to learn geometry sensitive features for semantic matching"
    - "accelerated nearest neighbor search with quick adc"
    - "multi image semantic matching by mining consistent features"
    - "multi image matching via fast alternating minimization"
    - "near optimal joint object matching via convex relaxation"
    - "hypercolumns for object segmentation and fine grained localization"

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.DS(Data Structures and Algorithms)