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: "domain invariant projection learning for zero shot recognition"
  Abstract: Zero-shot learning (ZSL) aims to recognize unseen object classes without any training samples, which can be regarded as a form of transfer learning from seen classes to unseen ones. This is made possible by learning a projection between a feature space and a semantic space (e.g. attribute space). Key to ZSL is thus to learn a projection function that is robust against the often large domain gap between the seen and unseen classes. In this paper, we propose a novel ZSL model termed domain-invariant projection learning (DIPL). Our model has two novel components: (1) A domain-invariant feature self-reconstruction task is introduced to the seen/unseen class data, resulting in a simple linear formulation that casts ZSL into a min-min optimization problem. Solving the problem is non-trivial, and a novel iterative algorithm is formulated as the solver, with rigorous theoretic algorithm analysis provided. (2) To further align the two domains via the learned projection, shared semantic structure among seen and unseen classes is explored via forming superclasses in the semantic space. Extensive experiments show that our model outperforms the state-of-the-art alternatives by significant margins.

Connected through hyperedge HE-1 (induced by source paper "meta learning across meta tasks for few shot learning"):
  with members:
    - "cross domain few shot classification via learned feature wise transformation"
    - "learning what and where to transfer"
    - "charting the right manifold manifold mixup for few shot learning"
    - "infinite mixture prototypes for few shot learning"
    - "on minimum discrepancy estimation for deep domain adaptation"
    - "generating classification weights with gnn denoising autoencoders for few shot learning"
    - "bridging theory and algorithm for domain adaptation"
    - "transductive episodic wise adaptive metric for few shot learning"

Connected through hyperedge HE-2 (induced by source paper "any shot object detection"):
  with members:
    - "anchor loss modulating loss scale based on prediction difficulty"
    - "f vaegan d2 a feature generating framework for any shot learning"
    - "few example object detection with model communication"
    - "zero shot detection"
    - "a closer look at few shot classification"
    - "zero shot object detection learning to simultaneously recognize and localize novel concepts"
    - "zero shot object detection by hybrid region embedding"
    - "polarity loss for zero shot object detection"

Connected through hyperedge HE-3 (induced by source paper "from anchor generation to distribution alignment learning a discriminative embedding space for zero shot recognition"):
  with members:
    - "zero shot kernel learning"
    - "transductive unbiased embedding for zero shot learning"
    - "semantic autoencoder for zero shot learning"
    - "zero shot learning a comprehensive evaluation of the good the bad and the ugly"
    - "semantically consistent regularization for zero shot recognition"
    - "feature generating networks for zero shot learning"
    - "learning a deep embedding model for zero shot learning"
    - "synthesized classifiers for zero shot learning"

Connected through hyperedge HE-4 (induced by source paper "transductive zero shot learning for 3d point cloud classification"):
  with members:
    - "deeppoint3d learning discriminative local descriptors using deep metric learning on 3d point clouds"
    - "blended convolution and synthesis for efficient discrimination of 3d shapes"
    - "mitigating the hubness problem for zero shot learning of 3d objects"
    - "representation learning on unit ball with 3d roto translational equivariance"
    - "zero shot learning of 3d point cloud objects"
    - "a theoretically sound upper bound on the triplet loss for improving the efficiency of deep distance metric learning"
    - "deep multiple instance learning for zero shot image tagging"
    - "zero shot object detection learning to simultaneously recognize and localize novel concepts"

Connected through hyperedge HE-5 (induced by source paper "learning for new visual environments with limited labels"):
  with members:
    - "multi modal ensemble classification for generalized zero shot learning"
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "generative dual adversarial network for generalized zero shot learning"
    - "meta learning with latent embedding optimization"
    - "generalized zero and few shot learning via aligned variational autoencoders"
    - "improved techniques for adversarial discriminative domain adaptation"
    - "discriminative learning of latent features for zero shot recognition"
    - "learning class prototypes via structure alignment for zero shot recognition"

Connected through hyperedge HE-6 (induced by source paper "atzsl defensive zero shot recognition in the presence of adversaries"):
  with members:
    - "convolutional prototype learning for zero shot recognition"
    - "hierarchical prototype learning for zero shot recognition"
    - "classifier and exemplar synthesis for zero shot learning"
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "generalized zero and few shot learning via aligned variational autoencoders"
    - "discriminative learning of latent features for zero shot recognition"
    - "adversarial vulnerability for any classifier"
    - "preserving semantic relations for zero shot learning"

Connected through hyperedge HE-7 (induced by source paper "joint concept matching based learning for zero shot recognition"):
  with members:
    - "structured analysis dictionary learning for image classification"
    - "a unified approach for conventional zero shot generalized zero shot and few shot learning"
    - "joint dictionaries for zero shot learning"
    - "zero shot learning via class conditioned deep generative models"
    - "a generative model for zero shot learning using conditional variational autoencoders"
    - "semantically consistent regularization for zero shot recognition"
    - "zero shot recognition using dual visual semantic mapping paths"
    - "feature generating networks for zero shot learning"

Connected through hyperedge HE-8 (induced by source paper "visual space optimization for zero shot learning"):
  with members:
    - "f vaegan d2 a feature generating framework for any shot learning"
    - "generative dual adversarial network for generalized zero shot learning"
    - "generalized zero and few shot learning via aligned variational autoencoders"
    - "learning class prototypes via structure alignment for zero shot recognition"
    - "transductive unbiased embedding for zero shot learning"
    - "semantic autoencoder for zero shot learning"
    - "a simple exponential family framework for zero shot learning"
    - "a generative model for zero shot learning using conditional variational autoencoders"

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)