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: "interpretable structure evolving lstm"
  Abstract: This paper develops a general framework for learning interpretable data representation via Long Short-Term Memory (LSTM) recurrent neural networks over hierarchal graph structures. Instead of learning LSTM models over the pre-fixed structures, we propose to further learn the intermediate interpretable multi-level graph structures in a progressive and stochastic way from data during the LSTM network optimization. We thus call this model the structure-evolving LSTM. In particular, starting with an initial element-level graph representation where each node is a small data element, the structure-evolving LSTM gradually evolves the multi-level graph representations by stochastically merging the graph nodes with high compatibilities along the stacked LSTM layers. In each LSTM layer, we estimate the compatibility of two connected nodes from their corresponding LSTM gate outputs, which is used to generate a merging probability. The candidate graph structures are accordingly generated where the nodes are grouped into cliques with their merging probabilities. We then produce the new graph structure with a Metropolis-Hasting algorithm, which alleviates the risk of getting stuck in local optimums by stochastic sampling with an acceptance probability. Once a graph structure is accepted, a higher-level graph is then constructed by taking the partitioned cliques as its nodes. During the evolving process, representation becomes more abstracted in higher-levels where redundant information is filtered out, allowing more efficient propagation of long-range data dependencies. We evaluate the effectiveness of structure-evolving LSTM in the application of semantic object parsing and demonstrate its advantage over state-of-the-art LSTM models on standard benchmarks.

Connected through hyperedge HE-1 (induced by source paper "fashion retrieval via graph reasoning networks on a similarity pyramid"):
  with members:
    - "modanet a large scale street fashion dataset with polygon annotations"
    - "netizen style commenting on fashion photos dataset and diversity measures"
    - "end to end deep kronecker product matching for person re identification"
    - "video2shop exactly matching clothes in videos to online shopping images"
    - "deep reasoning with knowledge graph for social relationship understanding"
    - "deep randomized ensembles for metric learning"
    - "improving the annotation of deepfashion images for fine grained attribute recognition"
    - "person re identification with deep similarity guided graph neural network"

Connected through hyperedge HE-2 (induced by source paper "learnable tree filter for structure preserving feature transform"):
  with members:
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "learning a discriminative feature network for semantic segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "deformable convnets v2 more deformable better results"
    - "context encoding for semantic segmentation"
    - "segmentation aware convolutional networks using local attention masks"
    - "normface l 2 hypersphere embedding for face verification"
    - "non local neural networks"

Connected through hyperedge HE-3 (induced by source paper "sr lstm state refinement for lstm towards pedestrian trajectory prediction"):
  with members:
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "two stream adaptive graph convolutional networks for skeleton based action recognition"
    - "mx lstm mixing tracklets and vislets to jointly forecast trajectories and head poses"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "videos as space time region graphs"
    - "vip cnn visual phrase guided convolutional neural network"
    - "attention is all you need"

Connected through hyperedge HE-4 (induced by source paper "grapy ml graph pyramid mutual learning for cross dataset human parsing"):
  with members:
    - "graphonomy universal human parsing via graph transfer learning"
    - "devil in the details towards accurate single and multiple human parsing"
    - "instance level human parsing via part grouping network"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "unified perceptual parsing for scene understanding"
    - "parsing r cnn for instance level human analysis"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "understanding humans in crowded scenes deep nested adversarial learning and a new benchmark for multi human parsing"

Connected through hyperedge HE-5 (induced by source paper "explaining models an empirical study of how explanations impact fairness judgment"):
  with members:
    - "it s reducing a human being to a percentage perceptions of justice in algorithmic decisions"
    - "how do humans understand explanations from machine learning systems an evaluation of the human interpretability of explanation"
    - "fairness and accountability design needs for algorithmic support in high stakes public sector decision making"
    - "explanation in artificial intelligence insights from the social sciences"
    - "counterfactual explanations without opening the black box automated decisions and the gdpr"
    - "a unified approach to interpreting model predictions"
    - "men also like shopping reducing gender bias amplification using corpus level constraints"
    - "fairness in learning classic and contextual bandits"

Connected through hyperedge HE-6 (induced by source paper "a three dimensional convolutional recurrent network for convective storm nowcasting"):
  with members:
    - "decomposing motion and content for natural video sequence prediction"
    - "deep learning for precipitation nowcasting a benchmark and a new model"
    - "convolutional lstm network a machine learning approach for precipitation nowcasting"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-7 (induced by source paper "searching for efficient multi scale architectures for dense image prediction"):
  with members:
    - "weakly and semi supervised human body part parsing via pose guided knowledge transfer"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "learning a discriminative feature network for semantic segmentation"
    - "personlab person pose estimation and instance segmentation with a bottom up part based geometric embedding model"
    - "regularized evolution for image classifier architecture search"
    - "darts differentiable architecture search"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "efficient neural architecture search via parameter sharing"

Connected through hyperedge HE-8 (induced by source paper "learning deep representations for semantic image parsing a comprehensive overview"):
  with members:
    - "large kernel matters improve semantic segmentation by global convolutional network"
    - "deep supervision for pancreatic cyst segmentation in abdominal ct scans"
    - "progressively diffused networks for semantic image segmentation"
    - "not all pixels are equal difficulty aware semantic segmentation via deep layer cascade"
    - "knowledge guided recurrent neural network learning for task oriented action prediction"
    - "label refinement network for coarse to fine semantic segmentation"
    - "instance level salient object segmentation"
    - "semantic instance segmentation via deep metric learning"

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)