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: "the slashdot zoo mining a social network with negative edges"
  Abstract: We analyse the corpus of user relationships of the Slashdot technology news site. The data was collected from the Slashdot Zoo feature where users of the website can tag other users as friends and foes, providing positive and negative endorsements. We adapt social network analysis techniques to the problem of negative edge weights. In particular, we consider signed variants of global network characteristics such as the clustering coefficient, node-level characteristics such as centrality and popularity measures, and link-level characteristics such as distances and similarity measures. We evaluate these measures on the task of identifying unpopular users, as well as on the task of predicting the sign of links and show that the network exhibits multiplicative transitivity which allows algebraic methods based on matrix multiplication to be used. We compare our methods to traditional methods which are only suitable for positively weighted edges.

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"
    - "sitting closer to friends than enemies revisited"

Connected through hyperedge HE-2 (induced by source paper "proximity preserving binary code using signed graph cut"):
  with members:
    - "sphereface deep hypersphere embedding for face recognition"
    - "facenet a unified embedding for face recognition and clustering"
    - "inductive hashing on manifolds"
    - "efficient estimation of word representations in vector space"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-3 (induced by source paper "improved mixing time for k subgraph sampling"):
  with members:
    - "waddling random walk fast and accurate mining of motif statistics in large graphs"
    - "escape efficiently counting all 5 vertex subgraphs"
    - "path sampling a fast and provable method for estimating 4 vertex subgraph counts"
    - "efficiently estimating motif statistics of large networks"

Connected through hyperedge HE-4 (induced by source paper "a game generative network framework with its application to relationship inference"):
  with members:
    - "finding friend and foe in multi agent games"
    - "a survey of signed network mining in social media"

Connected through hyperedge HE-5 (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-6 (induced by source paper "balance in signed bipartite networks"):
  with members:
    - "butterfly counting in bipartite networks"
    - "signed network modeling based on structural balance theory"
    - "sne signed network embedding"
    - "measuring and modeling bipartite graphs with community structure"
    - "brazilian congress structural balance analysis"
    - "a survey of signed network mining in social media"
    - "common neighbours and the local community paradigm for topological link prediction in bipartite networks"
    - "exploiting the structure of bipartite graphs for algebraic and spectral graph theory applications"

Connected through hyperedge HE-7 (induced by source paper "discovering polarized communities in signed networks"):
  with members:
    - "reducing controversy by connecting opposing views"
    - "signed link analysis in social media networks"
    - "a survey of signed network mining in social media"
    - "controversy and sentiment in online news"
    - "correlation clustering with constrained cluster sizes and extended weights bounds"
    - "influence diffusion dynamics and influence maximization in social networks with friend and foe relationships"
    - "correlation clustering with a fixed number of clusters"

Connected through hyperedge HE-8 (induced by source paper "node classification for signed networks using diffuse interface methods"):
  with members:
    - "spectral clustering of signed graphs via matrix power means"
    - "an mbo scheme for clustering and semi supervised clustering of signed networks"
    - "community detection in networks via nonlinear modularity eigenvectors"
    - "on spectral partitioning of signed graphs"
    - "sne signed network embedding"
    - "spectral theory of unsigned and signed graphs applications to graph clustering a survey"
    - "on the troll trust model for edge sign prediction in social networks"
    - "a survey of signed network mining in social media"

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.SI(Social and Information Networks)