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: "data poisoning attack against unsupervised node embedding methods"
  Abstract: Unsupervised node embedding methods (e.g., DeepWalk, LINE, and node2vec) have attracted growing interests given their simplicity and effectiveness. However, although these methods have been proved effective in a variety of applications, none of the existing work has analyzed the robustness of them. This could be very risky if these methods are attacked by an adversarial party. In this paper, we take the task of link prediction as an example, which is one of the most fundamental problems for graph analysis, and introduce a data positioning attack to node embedding methods. We give a complete characterization of attacker's utilities and present efficient solutions to adversarial attacks for two popular node embedding methods: DeepWalk and LINE. We evaluate our proposed attack model on multiple real-world graphs. Experimental results show that our proposed model can significantly affect the results of link prediction by slightly changing the graph structures (e.g., adding or removing a few edges). We also show that our proposed model is very general and can be transferable across different embedding methods. Finally, we conduct a case study on a coauthor network to better understand our attack method.

Connected through hyperedge HE-1 (induced by source paper "humpty dumpty controlling word meanings via corpus poisoning"):
  with members:
    - "towards a robust deep neural network in text domain a survey"
    - "universal adversarial triggers for attacking and analyzing nlp"
    - "cross lingual alignment of contextual word embeddings with applications to zero shot dependency parsing"
    - "understanding the origins of bias in word embeddings"
    - "adversarial attacks on node embeddings via graph poisoning"
    - "black box generation of adversarial text sequences to evade deep learning classifiers"
    - "deep text classification can be fooled"
    - "towards understanding linear word analogies"

Connected through hyperedge HE-2 (induced by source paper "attacking graph based classification via manipulating the graph structure"):
  with members:
    - "adversarial attacks on graph neural networks via meta learning"
    - "certifiable robustness and robust training for graph convolutional networks"
    - "adversarial attack on graph structured data"
    - "adversarial attacks on node embeddings via graph poisoning"
    - "poisoning attacks to graph based recommender systems"
    - "graph based security and privacy analytics via collective classification with joint weight learning and propagation"
    - "structure based sybil detection in social networks via local rule based propagation"
    - "manipulating machine learning poisoning attacks and countermeasures for regression learning"

Connected through hyperedge HE-3 (induced by source paper "unsupervised euclidean distance attack on network embedding"):
  with members:
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "ga based q attack on community detection"
    - "attack graph convolutional networks by adding fake nodes"
    - "target defense against link prediction based attacks via evolutionary perturbations"
    - "hiding individuals and communities in a social network"
    - "synthesizing robust adversarial examples"
    - "node2vec scalable feature learning for networks"
    - "don t walk skip online learning of multi scale network embeddings"

Connected through hyperedge HE-4 (induced by source paper "adversarial objects against lidar based autonomous driving systems"):
  with members:
    - "meshadv adversarial meshes for visual recognition"
    - "spatially transformed adversarial examples"
    - "generating 3d adversarial point clouds"
    - "characterizing adversarial examples based on spatial consistency information for semantic segmentation"
    - "generating adversarial examples with adversarial networks"
    - "robust physical world attacks on deep learning models"
    - "synthesizing robust adversarial examples"
    - "towards evaluating the robustness of neural networks"

Connected through hyperedge HE-5 (induced by source paper "towards data poisoning attack against knowledge graph embedding"):
  with members:
    - "adversarial attack on graph structured data"
    - "adversarial attacks on node embeddings via graph poisoning"
    - "semi supervised classification with graph convolutional networks"
    - "towards evaluating the robustness of neural networks"
    - "adversarial examples in the physical world"
    - "transa an adaptive approach for knowledge graph embedding"
    - "learning multi relational semantics using neural embedding models"
    - "embedding entities and relations for learning and inference in knowledge bases"

Connected through hyperedge HE-6 (induced by source paper "time aware gradient attack on dynamic network link prediction"):
  with members:
    - "exact k recommendation via maximal clique optimization"
    - "e lstm d a deep learning framework for dynamic network link prediction"
    - "attack graph convolutional networks by adding fake nodes"
    - "hiding individuals and communities in a social network"
    - "timers error bounded svd restart on dynamic networks"
    - "semi supervised classification with graph convolutional networks"
    - "line large scale information network embedding"
    - "deepwalk online learning of social representations"

Connected through hyperedge HE-7 (induced by source paper "data poisoning attack against knowledge graph embedding"):
  with members:
    - "adversarial attack on graph structured data"
    - "certified defenses for data poisoning attacks"
    - "semi supervised classification with graph convolutional networks"
    - "adversarial examples in the physical world"
    - "transa an adaptive approach for knowledge graph embedding"
    - "learning multi relational semantics using neural embedding models"
    - "embedding entities and relations for learning and inference in knowledge bases"
    - "poisoning attacks against support vector machines"

Connected through hyperedge HE-8 (induced by source paper "adversarial attack and defense on graph data a survey"):
  with members:
    - "spatially transformed adversarial examples"
    - "adversarial attack on graph structured data"
    - "adversarial attacks on node embeddings via graph poisoning"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "exploring the space of black box attacks on deep neural networks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "adversarial examples are not easily detected bypassing ten detection methods"

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)