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: "distributed consistent data association"
  Abstract: Data association is one of the fundamental problems in multi-sensor systems. Most current techniques rely on pairwise data associations which can be spurious even after the employment of outlier rejection schemes. Considering multiple pairwise associations at once significantly increases accuracy and leads to consistency. In this work, we propose two fully decentralized methods for consistent global data association from pairwise data associations. The first method is a consensus algorithm on the set of doubly stochastic matrices. The second method is a decentralization of the spectral method proposed by Pachauri et al.. We demonstrate the effectiveness of both methods using theoretical analysis and experimental evaluation.

Connected through hyperedge HE-1 (induced by source paper "all graphs lead to rome learning geometric and cycle consistent representations with graph convolutional networks"):
  with members:
    - "group normalization"
    - "relational inductive biases deep learning and graph networks"
    - "mimo graph filters for convolutional neural networks"
    - "convolutional neural networks via node varying graph filters"
    - "geometric deep learning going beyond euclidean data"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "learning to find good correspondences"
    - "multi image semantic matching by mining consistent features"

Connected through hyperedge HE-2 (induced by source paper "visual inertial scene representations"):
  with members:
    - "the cityscapes dataset for semantic urban scene understanding"
    - "semantic 3d reconstruction with continuous regularization and ray potentials using a visibility consistency constraint"
    - "deep learning of local rgb d patches for 3d object detection and 6d pose estimation"
    - "ssd single shot multibox detector"
    - "subcategory aware convolutional neural networks for object proposals and detection"
    - "exploring context with deep structured models for semantic segmentation"
    - "semanticfusion dense 3d semantic mapping with convolutional neural networks"
    - "deep sliding shapes for amodal 3d object detection in rgb d images"

Connected through hyperedge HE-3 (induced by source paper "distributable consistent multi object matching"):
  with members:
    - "graph matching with anchor nodes a learning approach"
    - "multi graph matching via affinity optimization with graduated consistency regularization"
    - "multi image matching via fast alternating minimization"
    - "near optimal joint object matching via convex relaxation"
    - "stable and informative spectral signatures for graph matching"

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.MA(Multiagent Systems)