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: "sitting closer to friends than enemies revisited"
  Abstract: Signed graphs, i.e., undirected graphs with edges labelled with a plus or minus sign, are commonly used to model relationships in social networks. Recently, Kermarrec and Thraves initiated the study of the problem of appropriately visualising the network: They asked whether any signed graph can be embedded into the metric space R^l in such a manner that every vertex is closer to all its friends (neighbours via positive edges) than to all its enemies (neighbours via negative edges). Interestingly, embeddability into R^1 can be expressed as a purely combinatorial problem. In this paper we pursue a deeper study of this particular case, answering several questions posed by Kermarrec and Thraves. #R##N#First, we refine the approach of Kermarrec and Thraves for the case of complete signed graphs by showing that the problem is closely related to the recognition of proper interval graphs. Second, we prove that the general case, whose polynomial-time tractability remained open, is in fact NP-complete. Finally, we provide lower and upper bounds for the time complexity of the general case: we prove that the existence of a subexponential time (in the number of vertices and edges of the input signed graph) algorithm would violate the Exponential Time Hypothesis, whereas a simple dynamic programming approach gives a running time single-exponential in the number of vertices.

Connected through hyperedge HE-1 (induced by source paper "deep network embedding for graph representation learning in signed networks"):
  with members:
    - "node2vec scalable feature learning for networks"
    - "a survey of signed network mining in social media"
    - "line large scale information network embedding"
    - "deepwalk online learning of social representations"
    - "negative link prediction in social media"
    - "efficient estimation of word representations in vector space"
    - "the slashdot zoo mining a social network with negative edges"

Connected through hyperedge HE-2 (induced by source paper "hyperbolic node embedding for signed networks"):
  with members:
    - "poincar e glove hyperbolic word embeddings"
    - "graph embedding techniques applications and performance a survey"
    - "hyperbolic entailment cones for learning hierarchical embeddings"
    - "sne signed network embedding"
    - "node2vec scalable feature learning for networks"
    - "a survey of signed network mining in social media"
    - "deepwalk online learning of social representations"
    - "exploiting social network structure for person to person sentiment analysis"

Connected through hyperedge HE-3 (induced by source paper "leveraging trust and distrust in recommender systems via deep learning"):
  with members:
    - "neural collaborative filtering"
    - "on sampling strategies for neural network based collaborative filtering"
    - "node2vec scalable feature learning for networks"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "deepwalk online learning of social representations"
    - "collaborative deep learning for recommender systems"
    - "bpr bayesian personalized ranking from implicit feedback"

Connected through hyperedge HE-4 (induced by source paper "a survey of signed network mining in social media"):
  with members:
    - "signed link analysis in social media networks"
    - "stochastic block model and exploratory analysis in signed networks"
    - "signed graph embedding when everybody can sit closer to friends than enemies"
    - "overlapping community detection in networks with positive and negative links"
    - "negative link prediction in social media"
    - "applications of structural balance in signed social networks"
    - "breaking the small cluster barrier of graph clustering"
    - "prediction and clustering in signed networks a local to global perspective"

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.DS(Data Structures and Algorithms)