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: "parsing occluded people by flexible compositions"
  Abstract: This paper presents an approach to parsing humans when there is significant occlusion. We model humans using a graphical model which has a tree structure building on recent work [32, 6] and exploit the connectivity prior that, even in presence of occlusion, the visible nodes form a connected subtree of the graphical model. We call each connected subtree a flexible composition of object parts. This involves a novel method for learning occlusion cues. During inference we need to search over a mixture of different flexible models. By exploiting part sharing, we show that this inference can be done extremely efficiently requiring only twice as many computations as searching for the entire object (i.e., not modeling occlusion). We evaluate our model on the standard benchmarked "We Are Family" Stickmen dataset and obtain significant performance improvements over the best alternative algorithms.

Connected through hyperedge HE-1 (induced by source paper "single stage multi person pose machines"):
  with members:
    - "fast and robust multi person 3d pose estimation from multiple views"
    - "lcr net multi person 2d and 3d pose detection in natural images"
    - "personlab person pose estimation and instance segmentation with a bottom up part based geometric embedding model"
    - "learning to refine human pose estimation"
    - "single shot multi person 3d pose estimation from monocular rgb"
    - "towards accurate multi person pose estimation in the wild"
    - "pose normalized image generation for person re identification"
    - "joint multi person pose estimation and semantic part segmentation"

Connected through hyperedge HE-2 (induced by source paper "crowdpose efficient crowded scenes pose estimation and a new benchmark"):
  with members:
    - "weakly and semi supervised human body part parsing via pose guided knowledge transfer"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "cornernet detecting objects as paired keypoints"
    - "pose flow efficient online pose tracking"
    - "personlab person pose estimation and instance segmentation with a bottom up part based geometric embedding model"
    - "yolov3 an incremental improvement"
    - "simple baselines for human pose estimation and tracking"
    - "pairwise body part attention for recognizing human object interactions"

Connected through hyperedge HE-3 (induced by source paper "joint multi person pose estimation and semantic part segmentation"):
  with members:
    - "semantic object parsing with graph lstm"
    - "stacked hourglass networks for human pose estimation"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "structured feature learning for pose estimation"
    - "deepercut a deeper stronger and faster multi person pose estimation model"
    - "joint object and part segmentation using deep learned potentials"
    - "attention to scale scale aware semantic image segmentation"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-4 (induced by source paper "occlusion aware hand pose estimation using hierarchical mixture density network"):
  with members:
    - "lifting from the deep convolutional 3d pose estimation from a single image"
    - "region ensemble network improving convolutional network for hand pose estimation"
    - "bighand2 2m benchmark hand pose dataset and state of the art analysis"
    - "deepprior improving fast and accurate 3d hand pose estimation"
    - "real time hand tracking under occlusion from an egocentric rgb d sensor"
    - "first person hand action benchmark with rgb d videos and 3d hand pose annotations"
    - "training a feedback loop for hand pose estimation"
    - "spatial attention deep net with partial pso for hierarchical hybrid hand pose estimation"

Connected through hyperedge HE-5 (induced by source paper "deep collaborative learning for visual recognition"):
  with members:
    - "wide residual networks"
    - "bilinear cnn models for fine grained visual recognition"
    - "neural activation constellations unsupervised part model discovery with convolutional networks"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "unsupervised learning of object semantic parts from internal states of cnns by population encoding"
    - "very deep convolutional networks for large scale image recognition"
    - "return of the devil in the details delving deep into convolutional nets"

Connected through hyperedge HE-6 (induced by source paper "a greedy part assignment algorithm for real time multi person 2d pose estimation"):
  with members:
    - "convolutional pose machines"
    - "stacked hourglass networks for human pose estimation"
    - "rmpe regional multi person pose estimation"
    - "joint graph decomposition and node labeling by local search"
    - "ssd single shot multibox detector"
    - "deepercut a deeper stronger and faster multi person pose estimation model"
    - "multi person pose estimation with local joint to person associations"
    - "realtime multi person 2d pose estimation using part affinity fields"

Connected through hyperedge HE-7 (induced by source paper "detecting semantic parts on partially occluded objects"):
  with members:
    - "amodal instance segmentation"
    - "neural activation constellations unsupervised part model discovery with convolutional networks"
    - "deep residual learning for image recognition"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "unsupervised learning of object semantic parts from internal states of cnns by population encoding"
    - "very deep convolutional networks for large scale image recognition"
    - "articulated pose estimation by a graphical model with image dependent pairwise relations"

Connected through hyperedge HE-8 (induced by source paper "cvpaper challenge in 2015 a review of cvpr2015 and deepsurvey"):
  with members:
    - "semi supervised learning with explicit relationship regularization"
    - "understanding image virality"
    - "watch and learn semi supervised learning of object detectors from videos"
    - "deep carving discovering visual attributes by carving deep neural nets"
    - "improving object detection with deep convolutional networks via bayesian optimization and structured prediction"
    - "bilinear random projections for locality sensitive binary codes"
    - "learning multiple visual tasks while discovering their structure"
    - "facenet a unified embedding for face recognition and clustering"

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)