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: "semisupervised feature analysis by mining correlations among multiple tasks"
  Abstract: In this paper, we propose a novel semisupervised feature selection framework by mining correlations among multiple tasks and apply it to different multimedia applications. Instead of independently computing the importance of features for each task, our algorithm leverages shared knowledge from multiple related tasks, thus improving the performance of feature selection. Note that the proposed algorithm is built upon an assumption that different tasks share some common structures. The proposed algorithm selects features in a batch mode, by which the correlations between various features are taken into consideration. Besides, considering the fact that labeling a large amount of training data in real world is both time-consuming and tedious, we adopt manifold learning, which exploits both labeled and unlabeled training data for a feature space analysis. Since the objective function is nonsmooth and difficult to solve, we propose an iteractive algorithm with fast convergence. Extensive experiments on different applications demonstrate that our algorithm outperforms the other state-of-the-art feature selection algorithms.

Connected through hyperedge HE-1 (induced by source paper "beyond without forgetting multi task learning for classification with disjoint datasets"):
  with members:
    - "mixmatch a holistic approach to semi supervised learning"
    - "curriculumnet weakly supervised learning from large scale web images"
    - "multi task multi label and multi domain learning with residual convolutional networks for emotion recognition"
    - "multi task learning of pairwise sequence classification tasks over disparate label spaces"
    - "universal representations the missing link between faces text planktons and cat breeds"
    - "learning from noisy labels with distillation"
    - "training deep networks for facial expression recognition with crowd sourced label distribution"
    - "from facial expression recognition to interpersonal relation prediction"

Connected through hyperedge HE-2 (induced by source paper "sim real joint reinforcement transfer for 3d indoor navigation"):
  with members:
    - "dm gan dynamic memory generative adversarial networks for text to image synthesis"
    - "video to video synthesis"
    - "adversarial learning with local coordinate coding"
    - "filter pruning via geometric median for deep convolutional neural networks acceleration"
    - "chalet cornell house agent learning environment"
    - "guided feature transformation gft a neural language grounding module for embodied agents"
    - "on evaluation of embodied navigation agents"
    - "building generalizable agents with a realistic and rich 3d environment"

Connected through hyperedge HE-3 (induced by source paper "exploring auxiliary context discrete semantic transfer hashing for scalable image retrieval"):
  with members:
    - "learning to hash for indexing big data a survey"
    - "supervised hashing using graph cuts and boosted decision trees"
    - "self taught hashing for fast similarity search"

Connected through hyperedge HE-4 (induced by source paper "depth information guided crowd counting for complex crowd scenes"):
  with members:
    - "compound rank k projections for bilinear analysis"
    - "crowdnet a deep convolutional network for dense crowd counting"
    - "stereo matching by training a convolutional neural network to compare image patches"
    - "towards scene understanding with detailed 3d object representations"
    - "depth map prediction from a single image using a multi scale deep network"
    - "predicting depth surface normals and semantic labels with a common multi scale convolutional architecture"

Connected through hyperedge HE-5 (induced by source paper "a discriminatively learned cnn embedding for person reidentification"):
  with members:
    - "enhancing person re identification in a self trained subspace"
    - "multichannel kernel canonical correlation analysis for cross view person reidentification"
    - "end to end comparative attention networks for person re identification"
    - "unlabeled samples generated by gan improve the person re identification baseline in vitro"
    - "gated siamese convolutional neural network architecture for human re identification"
    - "person re identification in the wild"
    - "learning a discriminative null space for person re identification"
    - "deep attributes driven multi camera person re identification"

Connected through hyperedge HE-6 (induced by source paper "part based deep hashing for large scale person re identification"):
  with members:
    - "improving person re identification by attribute and identity learning"
    - "end to end comparative attention networks for person re identification"
    - "unlabeled samples generated by gan improve the person re identification baseline in vitro"
    - "svdnet for pedestrian retrieval"
    - "temporal model adaptation for person re identification"
    - "deep attributes driven multi camera 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"

Connected through hyperedge HE-7 (induced by source paper "predicting rich drug drug interactions via biomedical knowledge graphs and text jointly embedding"):
  with members:
    - "pdd graph bridging electronic medical records and biomedical knowledge graphs via entity linking"
    - "holographic embeddings of knowledge graphs"
    - "adam a method for stochastic optimization"
    - "distributed representations of words and phrases and their compositionality"

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.LG(Machine Learning)