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: "robust attentional aggregation of deep feature sets for multi view 3d reconstruction"
  Abstract: We study the problem of recovering an underlying 3D shape from a set of images. Existing learning based approaches usually resort to recurrent neural nets, e.g., GRU, or intuitive pooling operations, e.g., max/mean poolings, to fuse multiple deep features encoded from input images. However, GRU based approaches are unable to consistently estimate 3D shapes given different permutations of the same set of input images as the recurrent unit is permutation variant. It is also unlikely to refine the 3D shape given more images due to the long-term memory loss of GRU. Commonly used pooling approaches are limited to capturing partial information, e.g., max/mean values, ignoring other valuable features. In this paper, we present a new feed-forward neural module, named AttSets, together with a dedicated training algorithm, named FASet, to attentively aggregate an arbitrarily sized deep feature set for multi-view 3D reconstruction. The AttSets module is permutation invariant, computationally efficient and flexible to implement, while the FASet algorithm enables the AttSets based network to be remarkably robust and generalize to an arbitrary number of input images. We thoroughly evaluate FASet and the properties of AttSets on multiple large public datasets. Extensive experiments show that AttSets together with FASet algorithm significantly outperforms existing aggregation approaches.

Connected through hyperedge HE-1 (induced by source paper "pointloc deep pose regressor for lidar point cloud localization"):
  with members:
    - "local supports global deep camera relocalization with sequence enhancement"
    - "the oxford radar robotcar dataset a radar extension to the oxford robotcar dataset"
    - "atloc attention guided camera localization"
    - "deep hough voting for 3d object detection in point clouds"
    - "deeppco end to end point cloud odometry through deep parallel neural network"
    - "deep auxiliary learning for visual localization and odometry"
    - "flownet3d learning scene flow in 3d point clouds"
    - "vlocnet deep multitask learning for semantic visual localization and odometry"

Connected through hyperedge HE-2 (induced by source paper "on the limitations of representing functions on sets"):
  with members:
    - "attentive neural processes"
    - "set transformer a framework for attention based permutation invariant neural networks"
    - "sequential attend infer repeat generative modelling of moving objects"
    - "consistent generative query networks"
    - "gspn generative shape proposal network for 3d instance segmentation in point cloud"
    - "janossy pooling learning deep permutation invariant functions for variable size inputs"
    - "attention is all you need"
    - "frustum pointnets for 3d object detection from rgb d data"

Connected through hyperedge HE-3 (induced by source paper "representing unordered data using multiset automata and complex numbers"):
  with members:
    - "on the limitations of representing functions on sets"
    - "algorithms and training for weighted multiset automata and regular expressions"
    - "deep sets"
    - "janossy pooling learning deep permutation invariant functions for variable size inputs"
    - "attention is all you need"
    - "convolutional sequence to sequence learning"
    - "exact solutions to the nonlinear dynamics of learning in deep linear neural networks"

Connected through hyperedge HE-4 (induced by source paper "set transformer a framework for attention based permutation invariant neural networks"):
  with members:
    - "on the limitations of representing functions on sets"
    - "attentive neural processes"
    - "attention based deep multiple instance learning"
    - "conditional neural processes"
    - "deep sets"
    - "attend and interact higher order object interactions for video understanding"
    - "attention is all you need"
    - "a simple neural network module for relational reasoning"

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.CV(Computer Vision and Pattern Recognition)