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: "deepsetnet predicting sets with deep neural networks"
  Abstract: This paper addresses the task of set prediction using deep learning. This is important because the output of many computer vision tasks, including image tagging and object detection, are naturally expressed as sets of entities rather than vectors. As opposed to a vector, the size of a set is not fixed in advance, and it is invariant to the ordering of entities within it. We define a likelihood for a set distribution and learn its parameters using a deep neural network. We also derive a loss for predicting a discrete distribution corresponding to set cardinality. Set prediction is demonstrated on the problem of multi-class image classification. Moreover, we show that the proposed cardinality loss can also trivially be applied to the tasks of object counting and pedestrian detection. Our approach outperforms existing methods in all three cases on standard datasets.

Connected through hyperedge HE-1 (induced by source paper "learn to predict sets using feed forward neural networks"):
  with members:
    - "rep the set neural networks for learning set representations"
    - "deep set prediction networks"
    - "learning pairwise relationship for multi object detection in crowded scenes"
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "yolov3 an incremental improvement"
    - "deep sets"
    - "deep perm set net learn to predict sets with unknown permutation and cardinality using deep neural networks"
    - "janossy pooling learning deep permutation invariant functions for variable size inputs"

Connected through hyperedge HE-2 (induced by source paper "compositional embeddings for multi label one shot learning"):
  with members:
    - "semeval 2016 task 4 sentiment analysis in twitter"
    - "adapted deep embeddings a synthesis of methods for k shot inductive transfer learning"
    - "pair2vec compositional word pair embeddings for cross sentence inference"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "generalization without systematicity on the compositional skills of sequence to sequence recurrent networks"
    - "learning feed forward one shot learners"
    - "deep residual learning for image recognition"
    - "facenet a unified embedding for face recognition and clustering"

Connected through hyperedge HE-3 (induced by source paper "quaternion equivariant capsule networks for 3d point clouds"):
  with members:
    - "equivariant multi view networks"
    - "spherical cnns on unstructured grids"
    - "spherical regression learning viewpoints surface normals and 3d rotations on n spheres"
    - "gauge equivariant convolutional networks and the icosahedral cnn"
    - "learning an effective equivariant 3d descriptor without supervision"
    - "pointnetlk robust efficient point cloud registration using pointnet"
    - "equivariant neural networks and equivarification"
    - "ppfnet global context aware local features for robust 3d point matching"

Connected through hyperedge HE-4 (induced by source paper "elucidating image to set prediction an analysis of models losses and datasets"):
  with members:
    - "on the iterative refinement of densely connected representation levels for semantic segmentation"
    - "multi label learning with missing labels using mixed dependency graphs"
    - "exploring the limits of weakly supervised pretraining"
    - "multi label image classification via knowledge distillation from weakly supervised detection"
    - "loss functions for multiset prediction"
    - "attention is all you need"
    - "multi label image recognition by recurrently discovering attentional regions"
    - "learning deep latent spaces for multi label classification"

Connected through hyperedge HE-5 (induced by source paper "learning pairwise relationship for multi object detection in crowded scenes"):
  with members:
    - "soft nms improving object detection with one line of code"
    - "pixelwise instance segmentation with a dynamically instantiated network"
    - "learning non maximum suppression"
    - "deep watershed transform for instance segmentation"
    - "feature pyramid networks for object detection"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "motchallenge 2015 towards a benchmark for multi target tracking"

Connected through hyperedge HE-6 (induced by source paper "learning functions over sets via permutation adversarial networks"):
  with members:
    - "learning representations of sets through optimized permutations"
    - "janossy pooling learning deep permutation invariant functions for variable size inputs"
    - "deeppermnet visual permutation learning"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "streaming pca matching matrix bernstein and near optimal finite sample guarantees for oja s algorithm"
    - "semi supervised classification with graph convolutional networks"
    - "diffusion convolutional neural networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "exchangeable generative models with flow scans"):
  with members:
    - "set transformer a framework for attention based permutation invariant neural networks"
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "deep sets"
    - "point cloud gan"
    - "neural autoregressive distribution estimation"
    - "density estimation using real nvp"
    - "tensorflow a system for large scale machine learning"
    - "made masked autoencoder for distribution estimation"

Connected through hyperedge HE-8 (induced by source paper "a weakly supervised learning based clustering framework"):
  with members:
    - "learned cardinalities estimating correlated joins with deep learning"
    - "a compact network learning model for distribution regression"
    - "predicting time varying distributions with limited training data"
    - "deep sets"
    - "predict and constrain modeling cardinality in deep structured prediction"
    - "deep unsupervised clustering with gaussian mixture variational autoencoders"
    - "group equivariant convolutional networks"
    - "joint unsupervised learning of deep representations and image clusters"

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)