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: "person search with natural language description"
  Abstract: Searching persons in large-scale image databases with the query of natural language description has important applications in video surveillance. Existing methods mainly focused on searching persons with image-based or attribute-based queries, which have major limitations for a practical usage. In this paper, we study the problem of person search with natural language description. Given the textual description of a person, the algorithm of the person search is required to rank all the samples in the person database then retrieve the most relevant sample corresponding to the queried description. Since there is no person dataset or benchmark with textual description available, we collect a large-scale person description dataset with detailed natural language annotations and person samples from various sources, termed as CUHK Person Description Dataset (CUHK-PEDES). A wide range of possible models and baselines have been evaluated and compared on the person search benchmark. An Recurrent Neural Network with Gated Neural Attention mechanism (GNA-RNN) is proposed to establish the state-of-the art performance on person search.

Connected through hyperedge HE-1 (induced by source paper "cross modality person re identification with shared specific feature transfer"):
  with members:
    - "interaction and aggregation network for person re identification"
    - "hpiln a feature learning framework for cross modality person re identification"
    - "learning disentangled representation for robust person re identification"
    - "a new benchmark and approach for fine grained cross media retrieval"
    - "joint discriminative and generative learning for person re identification"
    - "beyond intra modality discrepancy a comprehensive survey of heterogeneous person re identification"
    - "mtfh a matrix tri factorization hashing framework for efficient cross modal retrieval"
    - "attend to the difference cross modality person re identification via contrastive correlation"

Connected through hyperedge HE-2 (induced by source paper "a convolutional baseline for person re identification using vision and language descriptions"):
  with members:
    - "deep representation learning with part loss for person re identification"
    - "improving person re identification by attribute and identity learning"
    - "touchdown natural language navigation and spatial reasoning in visual street environments"
    - "attention aware compositional network for person re identification"
    - "stacked cross attention for image text matching"
    - "pose guided joint global and attentive local matching network for text based person search"
    - "look imagine and match improving textual visual cross modal retrieval with generative models"
    - "dual path convolutional image text embedding with instance loss"

Connected through hyperedge HE-3 (induced by source paper "mutatt visual textual mutual guidance for referring expression comprehension"):
  with members:
    - "learning to collocate neural modules for image captioning"
    - "dynamic graph attention for referring expression comprehension"
    - "visual semantic reasoning for image text matching"
    - "neighbourhood watch referring expression comprehension via language guided graph attention networks"
    - "mattnet modular attention network for referring expression comprehension"
    - "rethinking diversified and discriminative proposal generation for visual grounding"
    - "visual reasoning with multi hop feature modulation"
    - "identity aware textual visual matching with latent co attention"

Connected through hyperedge HE-4 (induced by source paper "interactive natural language based person search"):
  with members:
    - "an empirical study of person re identification with attributes"
    - "pythia v0 1 the winning entry to the vqa challenge 2018"
    - "identity aware textual visual matching with latent co attention"
    - "co attending free form regions and detections with multi modal multiplicative feature embedding for visual question answering"
    - "adversarial attribute image person re identification"
    - "mask r cnn"
    - "deep attributes driven multi camera person re identification"
    - "vqa visual question answering"

Connected through hyperedge HE-5 (induced by source paper "weakly supervised person re id differentiable graphical learning and a new benchmark"):
  with members:
    - "unsupervised person re identification by deep asymmetric metric embedding"
    - "aanet attribute attention network for person re identifications"
    - "joint discriminative and generative learning for person re identification"
    - "unsupervised person re identification by soft multilabel learning"
    - "adaptively connected neural networks"
    - "invariance matters exemplar memory for domain adaptive person re identification"
    - "discovering underlying person structure pattern with relative local distance for person re identification"
    - "perceive where to focus learning visibility aware part level features for partial person re identification"

Connected through hyperedge HE-6 (induced by source paper "adversarial representation learning for text to image matching"):
  with members:
    - "d sne domain adaptation using stochastic neighborhood embedding"
    - "mirrorgan learning text to image generation by redescription"
    - "in defense of the triplet loss for visual recognition"
    - "boosting standard classification architectures through a ranking regularizer"
    - "man moment alignment network for natural language moment retrieval via iterative graph adjustment"
    - "partial adversarial domain adaptation"
    - "deep contextualized word representations"
    - "learnable pins cross modal embeddings for person identity"

Connected through hyperedge HE-7 (induced by source paper "improving person re identification by attribute and identity learning"):
  with members:
    - "deep adaptive feature embedding with local sample distributions for person re identification"
    - "attention aware compositional network for person re identification"
    - "transferable joint attribute identity deep learning for unsupervised person re identification"
    - "deep spatial feature reconstruction for partial person re identification alignment free approach"
    - "a discriminatively learned cnn embedding for person reidentification"
    - "looking beyond appearances synthetic training data for deep cnns in re identification"
    - "adversarial attribute image person re identification"
    - "end to end comparative attention networks for person re identification"

Connected through hyperedge HE-8 (induced by source paper "improving description based person re identification by multi granularity image text alignments"):
  with members:
    - "deep representation learning with part loss for person re identification"
    - "stacked cross attention for image text matching"
    - "pamm pose aware multi shot matching for improving person re identification"
    - "dual path convolutional image text embedding with instance loss"
    - "convolutional image captioning"
    - "vse improving visual semantic embeddings with hard negatives"
    - "learning semantic concepts and order for image and sentence matching"
    - "tips and tricks for visual question answering learnings from the 2017 challenge"

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)