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: "attributes2classname a discriminative model for attribute based unsupervised zero shot learning"
  Abstract: We propose a novel approach for unsupervised zero-shot learning (ZSL) of classes based on their names. Most existing unsupervised ZSL methods aim to learn a model for directly comparing image features and class names. However, this proves to be a difficult task due to dominance of non-visual semantics in underlying vector-space embeddings of class names. To address this issue, we discriminatively learn a word representation such that the similarities between class and combination of attribute names fall in line with the visual similarity. Contrary to the traditional zero-shot learning approaches that are built upon attribute presence, our approach bypasses the laborious attribute-class relation annotations for unseen classes. In addition, our proposed approach renders text-only training possible, hence, the training can be augmented without the need to collect additional image data. The experimental results show that our method yields state-of-the-art results for unsupervised ZSL in three benchmark datasets.

Connected through hyperedge HE-1 (induced by source paper "zero shot learning of 3d point cloud objects"):
  with members:
    - "spidercnn deep learning on point sets with parameterized convolutional filters"
    - "local spectral graph convolution for point set feature learning"
    - "so net self organizing network for point cloud analysis"
    - "dynamic graph cnn for learning on point clouds"
    - "pointnetvlad deep point cloud based retrieval for large scale place recognition"
    - "a unified approach for conventional zero shot generalized zero shot and few shot learning"
    - "multi label zero shot learning with structured knowledge graphs"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"

Connected through hyperedge HE-2 (induced by source paper "zero shot object detection learning to simultaneously recognize and localize novel concepts"):
  with members:
    - "a unified approach for conventional zero shot generalized zero shot and few shot learning"
    - "semantic autoencoder for zero shot learning"
    - "semantically consistent regularization for zero shot recognition"
    - "zero shot recognition using dual visual semantic mapping paths"
    - "zero shot learning the good the bad and the ugly"
    - "recovering the missing link predicting class attribute associations for unsupervised zero shot learning"
    - "learning a deep embedding model for zero shot learning"
    - "ssd single shot multibox detector"

Connected through hyperedge HE-3 (induced by source paper "deep multiple instance learning for zero shot image tagging"):
  with members:
    - "zero shot object detection learning to simultaneously recognize and localize novel concepts"
    - "a unified approach for conventional zero shot generalized zero shot and few shot learning"
    - "semantic autoencoder for zero shot learning"
    - "semantically consistent regularization for zero shot recognition"
    - "zero shot recognition using dual visual semantic mapping paths"
    - "zero shot learning the good the bad and the ugly"
    - "deep patch learning for weakly supervised object classification and discovery"
    - "hcp a flexible cnn framework for multi label image classification"

Connected through hyperedge HE-4 (induced by source paper "zero shot sketch image hashing"):
  with members:
    - "semantic autoencoder for zero shot learning"
    - "deep sketch hashing fast free hand sketch based image retrieval"
    - "stochastic generative hashing"
    - "zero shot recognition using dual visual semantic mapping paths"
    - "deep binaries encoding semantic rich cues for efficient textual visual cross retrieval"
    - "multi modal factorized bilinear pooling with co attention learning for visual question answering"
    - "recovering the missing link predicting class attribute associations for unsupervised zero shot learning"
    - "learning a deep embedding model for zero shot learning"

Connected through hyperedge HE-5 (induced by source paper "transductive unbiased embedding for zero shot learning"):
  with members:
    - "visually aligned word embeddings for improving zero shot learning"
    - "zero shot learning a comprehensive evaluation of the good the bad and the ugly"
    - "semantically consistent regularization for zero shot recognition"
    - "learning a deep embedding model for zero shot learning"
    - "gaze embeddings for zero shot image classification"
    - "densely connected convolutional networks"
    - "synthesized classifiers for zero shot learning"
    - "semi supervised vocabulary informed learning"

Connected through hyperedge HE-6 (induced by source paper "learning class prototypes via structure alignment for zero shot recognition"):
  with members:
    - "semantic autoencoder for zero shot learning"
    - "semantically consistent regularization for zero shot recognition"
    - "zero shot learning the good the bad and the ugly"
    - "learning deep representations of fine grained visual descriptions"
    - "learning a deep embedding model for zero shot learning"
    - "synthesized classifiers for zero shot learning"
    - "improving semantic embedding consistency by metric learning for zero shot classification"
    - "predicting visual exemplars of unseen classes for zero shot learning"

Connected through hyperedge HE-7 (induced by source paper "zero shot visual recognition using semantics preserving adversarial embedding networks"):
  with members:
    - "semantic autoencoder for zero shot learning"
    - "a generative model for zero shot learning using conditional variational autoencoders"
    - "video question answering via attribute augmented attention network learning"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "adversarial discriminative domain adaptation"
    - "natural and effective obfuscation by head inpainting"
    - "unified deep supervised domain adaptation and generalization"
    - "unpaired image to image translation using cycle consistent adversarial networks"

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)