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: "deep perm set net learn to predict sets with unknown permutation and cardinality using deep neural networks"
  Abstract: Many real-world problems, e.g. object detection, have outputs that are naturally expressed as sets of entities. This creates a challenge for traditional deep neural networks which naturally deal with structured outputs such as vectors, matrices or tensors. We present a novel approach for learning to predict sets with unknown permutation and cardinality using deep neural networks. Specifically, in our formulation we incorporate the permutation as unobservable variable and estimate its distribution during the learning process using alternating optimization. We demonstrate the validity of this new formulation on two relevant vision problems: object detection, for which our formulation outperforms state-of-the-art detectors such as Faster R-CNN and YOLO, and a complex CAPTCHA test, where we observe that, surprisingly, our set based network acquired the ability of mimicking arithmetics without any rules being coded.

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"
    - "janossy pooling learning deep permutation invariant functions for variable size inputs"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"

Connected through hyperedge HE-2 (induced by source paper "set structured latent representations"):
  with members:
    - "integrating learning and reasoning with deep logic models"
    - "fspool learning set representations with featurewise sort pooling"
    - "are disentangled representations helpful for abstract visual reasoning"
    - "deep set prediction networks"
    - "multi object representation learning with iterative variational inference"
    - "videograph recognizing minutes long human activities in videos"
    - "set transformer a framework for attention based permutation invariant neural networks"
    - "object based reasoning in vqa"

Connected through hyperedge HE-3 (induced by source paper "fspool learning set representations with featurewise sort pooling"):
  with members:
    - "deep set prediction networks"
    - "sorting out lipschitz function approximation"
    - "learning representations of sets through optimized permutations"
    - "hierarchical graph representation learning with differentiable pooling"
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "deep sets"
    - "compositional attention networks for machine reasoning"
    - "how powerful are graph neural networks"

Connected through hyperedge HE-4 (induced by source paper "deep set prediction networks"):
  with members:
    - "fspool learning set representations with featurewise sort pooling"
    - "multi object representation learning with iterative variational inference"
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "learning representations of sets through optimized permutations"
    - "object based reasoning in vqa"
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "deep sets"
    - "learning deep generative models of graphs"

Connected through hyperedge HE-5 (induced by source paper "auto labelling of markers in optical motion capture by permutation learning"):
  with members:
    - "deeppermnet visual permutation learning"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-6 (induced by source paper "metaanchor learning to detect objects with customized anchors"):
  with members:
    - "detnet a backbone network for object detection"
    - "sface an efficient network for face detection in large scale variations"
    - "beyond trade off accelerate fcn based face detector with higher accuracy"
    - "fssd feature fusion single shot multibox detector"
    - "s 3 fd single shot scale invariant face detector"
    - "focal loss for dense object detection"
    - "what can help pedestrian detection"
    - "dssd deconvolutional single shot detector"

Connected through hyperedge HE-7 (induced by source paper "janossy pooling learning deep permutation invariant functions for variable size inputs"):
  with members:
    - "hierarchical graph representation learning with differentiable pooling"
    - "representation learning on graphs with jumping knowledge networks"
    - "geniepath graph neural networks with adaptive receptive paths"
    - "relational inductive biases deep learning and graph networks"
    - "fastgcn fast learning with graph convolutional networks via importance sampling"
    - "how powerful are graph neural networks"
    - "interpretable set functions"
    - "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)