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: "prism person re identification via structured matching"
  Abstract: Person re-identification (re-id), an emerging problem in visual surveillance, deals with maintaining entities of individuals whilst they traverse various locations surveilled by a camera network. From a visual perspective re-id is challenging due to significant changes in visual appearance of individuals in cameras with different pose, illumination and calibration. Globally the challenge arises from the need to maintain structurally consistent matches among all the individual entities across different camera views. We propose PRISM, a structured matching method to jointly account for these challenges. We view the global problem as a weighted graph matching problem and estimate edge weights by learning to predict them based on the co-occurrences of visual patterns in the training examples. These co-occurrence based scores in turn account for appearance changes by inferring likely and unlikely visual co-occurrences appearing in training instances. We implement PRISM on single shot and multi-shot scenarios. PRISM uniformly outperforms state-of-the-art in terms of matching rate while being computationally efficient.

Connected through hyperedge HE-1 (induced by source paper "learning correspondence structures for person re identification"):
  with members:
    - "learning a discriminative null space for person re identification"
    - "learning deep feature representations with domain guided dropout for person re identification"
    - "deep ranking for person re identification via joint representation learning"
    - "an enhanced deep feature representation for person re identification"
    - "deep feature learning with relative distance comparison for person re identification"
    - "learning descriptors for object recognition and 3d pose estimation"
    - "person re identification with correspondence structure learning"
    - "bit scalable deep hashing with regularized similarity learning for image retrieval and person re identification"

Connected through hyperedge HE-2 (induced by source paper "supervised hashing with deep neural networks"):
  with members:
    - "learning both weights and connections for efficient neural networks"
    - "deep semantic ranking based hashing for multi label image retrieval"
    - "hashing with binary autoencoders"
    - "group membership prediction"
    - "towards biologically plausible deep learning"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "zero shot learning via semantic similarity embedding"
    - "an exploration of parameter redundancy in deep networks with circulant projections"

Connected through hyperedge HE-3 (induced by source paper "zero shot learning via semantic similarity embedding"):
  with members:
    - "constructing the l2 graph for robust subspace learning and subspace clustering"
    - "probabilistic zero shot classification with semantic rankings"
    - "group membership prediction"
    - "evaluation of output embeddings for fine grained image classification"
    - "zero shot recognition with unreliable attributes"
    - "very deep convolutional networks for large scale image recognition"
    - "matconvnet convolutional neural networks for matlab"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-4 (induced by source paper "zero shot learning via joint latent similarity embedding"):
  with members:
    - "group membership prediction"
    - "evaluation of output embeddings for fine grained image classification"
    - "zero shot learning via semantic similarity embedding"
    - "predicting deep zero shot convolutional neural networks using textual descriptions"
    - "zero shot recognition with unreliable attributes"
    - "very deep convolutional networks for large scale image recognition"
    - "a novel visual word co occurrence model for person re identification"
    - "decaf a deep convolutional activation feature for generic visual recognition"

Connected through hyperedge HE-5 (induced by source paper "group membership prediction"):
  with members:
    - "zero shot learning via semantic similarity embedding"
    - "tri subject kinship verification understanding the core of a family"
    - "a novel visual word co occurrence model for person re identification"
    - "a survey on multi view 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)