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: "recognizing partial biometric patterns"
  Abstract: Biometric recognition on partial captured targets is challenging, where only several partial observations of objects are available for matching. In this area, deep learning based methods are widely applied to match these partial captured objects caused by occlusions, variations of postures or just partial out of view in person re-identification and partial face recognition. However, most current methods are not able to identify an individual in case that some parts of the object are not obtainable, while the rest are specialized to certain constrained scenarios. To this end, we propose a robust general framework for arbitrary biometric matching scenarios without the limitations of alignment as well as the size of inputs. We introduce a feature post-processing step to handle the feature maps from FCN and a dictionary learning based Spatial Feature Reconstruction (SFR) to match different sized feature maps in this work. Moreover, the batch hard triplet loss function is applied to optimize the model. The applicability and effectiveness of the proposed method are demonstrated by the results from experiments on three person re-identification datasets (Market1501, CUHK03, DukeMTMC-reID), two partial person datasets (Partial REID and Partial iLIDS) and two partial face datasets (CASIA-NIR-Distance and Partial LFW), on which state-of-the-art performance is ensured in comparison with several state-of-the-art approaches. The code is released online and can be found on the website: this https URL.

Connected through hyperedge HE-1 (induced by source paper "high order information matters learning relation and topology for occluded person re identification"):
  with members:
    - "learning combinatorial embedding networks for deep graph matching"
    - "deep high resolution representation learning for human pose estimation"
    - "a novel teacher student learning framework for occluded person re identification"
    - "stnreid deep convolutional networks with pairwise spatial transformer networks for partial person re identification"
    - "bag of tricks and a strong baseline for deep person re identification"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "maskreid a mask based deep ranking neural network for person re identification"
    - "scpnet spatial channel parallelism network for joint holistic and partial person re identification"

Connected through hyperedge HE-2 (induced by source paper "semantics aligned representation learning for person re identification"):
  with members:
    - "deep representation learning with part loss for person re identification"
    - "re identification supervised texture generation"
    - "densebody directly regressing dense 3d human pose and shape from a single color image"
    - "improving person re identification by attribute and identity learning"
    - "learning discriminative features with multiple granularities for person re identification"
    - "maskreid a mask based deep ranking neural network for person re identification"
    - "densereg fully convolutional dense shape regression in the wild"
    - "attention aware compositional network for person re identification"

Connected through hyperedge HE-3 (induced by source paper "relation aware global attention"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "residual non local attention networks for image restoration"
    - "learning discriminative features with multiple granularities for person re identification"
    - "ocnet object context network for scene parsing"
    - "maskreid a mask based deep ranking neural network for person re identification"
    - "horizontal pyramid matching for person re identification"
    - "dual attention matching network for context aware feature sequence based person re identification"
    - "survey on deep learning techniques for person re identification task"

Connected through hyperedge HE-4 (induced by source paper "perceive where to focus learning visibility aware part level features for partial person re identification"):
  with members:
    - "deep representation learning with part loss for person re identification"
    - "occluded person re identification"
    - "human semantic parsing for person re identification"
    - "deep spatial feature reconstruction for partial person re identification alignment free approach"
    - "transitive invariance for self supervised visual representation learning"
    - "in defense of the triplet loss for person re identification"
    - "look into person self supervised structure sensitive learning and a new benchmark for human parsing"
    - "glad global local alignment descriptor for pedestrian retrieval"

Connected through hyperedge HE-5 (induced by source paper "stnreid deep convolutional networks with pairwise spatial transformer networks for partial person re identification"):
  with members:
    - "features for multi target multi camera tracking and re identification"
    - "partial person re identification with alignment and hallucination"
    - "deep spatial feature reconstruction for partial person re identification alignment free approach"
    - "in defense of the triplet loss for person re identification"
    - "margin sample mining loss a deep learning based method for person re identification"
    - "a pose sensitive embedding for person re identification with expanded cross neighborhood re ranking"
    - "glad global local alignment descriptor for pedestrian retrieval"
    - "alignedreid surpassing human level performance in person re identification"

Connected through hyperedge HE-6 (induced by source paper "densely semantically aligned person re identification"):
  with members:
    - "deep representation learning with part loss for person re identification"
    - "improving person re identification by attribute and identity learning"
    - "learning discriminative features with multiple granularities for person re identification"
    - "harmonious attention network for person re identification"
    - "maskreid a mask based deep ranking neural network for person re identification"
    - "densereg fully convolutional dense shape regression in the wild"
    - "attention aware compositional network for person re identification"
    - "dual attention matching network for context aware feature sequence based person re identification"

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)