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: "look into person self supervised structure sensitive learning and a new benchmark for human parsing"
  Abstract: Human parsing has recently attracted a lot of research interests due to its huge application potentials. However existing datasets have limited number of images and annotations, and lack the variety of human appearances and the coverage of challenging cases in unconstrained environment. In this paper, we introduce a new benchmark "Look into Person (LIP)" that makes a significant advance in terms of scalability, diversity and difficulty, a contribution that we feel is crucial for future developments in human-centric analysis. This comprehensive dataset contains over 50,000 elaborately annotated images with 19 semantic part labels, which are captured from a wider range of viewpoints, occlusions and background complexity. Given these rich annotations we perform detailed analyses of the leading human parsing approaches, gaining insights into the success and failures of these methods. Furthermore, in contrast to the existing efforts on improving the feature discriminative capability, we solve human parsing by exploring a novel self-supervised structure-sensitive learning approach, which imposes human pose structures into parsing results without resorting to extra supervision (i.e., no need for specifically labeling human joints in model training). Our self-supervised learning framework can be injected into any advanced neural networks to help incorporate rich high-level knowledge regarding human joints from a global perspective and improve the parsing results. Extensive evaluations on our LIP and the public PASCAL-Person-Part dataset demonstrate the superiority of our method.

Connected through hyperedge HE-1 (induced by source paper "hierarchical human parsing with typed part relation reasoning"):
  with members:
    - "learning compositional neural information fusion for human parsing"
    - "zero shot video object segmentation via attentive graph neural networks"
    - "graspnet a large scale clustered and densely annotated datase for object grasping"
    - "reasoning visual dialogs with structural and partial observations"
    - "understanding human gaze communication by spatio temporal graph reasoning"
    - "graphonomy universal human parsing via graph transfer learning"
    - "spgnet semantic prediction guidance for scene parsing"
    - "weakly and semi supervised human body part parsing via pose guided knowledge transfer"

Connected through hyperedge HE-2 (induced by source paper "magnifiernet towards semantic regularization and fusion for person re identification"):
  with members:
    - "interaction and aggregation network for person re identification"
    - "beyond human parts dual part aligned representations for person re identification"
    - "aanet attribute attention network for person re identifications"
    - "joint discriminative and generative learning for person re identification"
    - "omni scale feature learning for person re identification"
    - "foreground aware pyramid reconstruction for alignment free occluded person re identification"
    - "bag of tricks and a strong baseline for deep person re identification"
    - "improving person re identification by attribute and identity learning"

Connected through hyperedge HE-3 (induced by source paper "learning compositional neural information fusion for human parsing"):
  with members:
    - "weakly and semi supervised human body part parsing via pose guided knowledge transfer"
    - "instance level human parsing via part grouping network"
    - "crowdpose efficient crowded scenes pose estimation and a new benchmark"
    - "relational inductive biases deep learning and graph networks"
    - "learning human object interactions by graph parsing neural networks"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "macro micro adversarial network for human parsing"
    - "holistic instance level human parsing"

Connected through hyperedge HE-4 (induced by source paper "deep portrait image completion and extrapolation"):
  with members:
    - "synthesizing images of humans in unseen poses"
    - "look into person joint body parsing pose estimation network and a new benchmark"
    - "everybody dance now"
    - "generative image inpainting with contextual attention"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "generative face completion"
    - "a generative model of people in clothing"
    - "be your own prada fashion synthesis with structural coherence"

Connected through hyperedge HE-5 (induced by source paper "garmentgan photo realistic adversarial fashion transfer"):
  with members:
    - "unpaired pose guided human image generation"
    - "compatible and diverse fashion image inpainting"
    - "semantic image synthesis with spatially adaptive normalization"
    - "unsupervised person image synthesis in arbitrary poses"
    - "instance level human parsing via part grouping network"
    - "toward characteristic preserving image based virtual try on network"
    - "spectral normalization for generative adversarial networks"
    - "viton an image based virtual try on network"

Connected through hyperedge HE-6 (induced by source paper "universal rcnn universal object detector via transferable graph r cnn"):
  with members:
    - "upsnet a unified panoptic segmentation network"
    - "feature selective anchor free module for single shot object detection"
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "iterative visual reasoning beyond convolutions"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "taskonomy disentangling task transfer learning"
    - "frage frequency agnostic word representation"
    - "detnet a backbone network for object detection"

Connected through hyperedge HE-7 (induced by source paper "night time semantic segmentation with a large real dataset"):
  with members:
    - "guided curriculum model adaptation and uncertainty aware evaluation for semantic nighttime image segmentation"
    - "curriculum model adaptation with synthetic and real data for semantic foggy scene understanding"
    - "expectation maximization attention networks for semantic segmentation"
    - "gated scnn gated shape cnns for semantic segmentation"
    - "spgnet semantic prediction guidance for scene parsing"
    - "model adaptation with synthetic and real data for semantic dense foggy scene understanding"
    - "espnet efficient spatial pyramid of dilated convolutions for semantic segmentation"
    - "learning to adapt structured output space for semantic segmentation"

Connected through hyperedge HE-8 (induced by source paper "knowledge integration networks for action recognition"):
  with members:
    - "geometric pose affordance 3d human pose with scene constraints"
    - "eco efficient convolutional network for online video understanding"
    - "temporal bilinear networks for video action recognition"
    - "stnet local and global spatial temporal modeling for action recognition"
    - "actor action semantic segmentation with region masks"
    - "videos as space time region graphs"
    - "slowfast networks for video recognition"
    - "quo vadis action recognition a new model and the kinetics dataset"

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)