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: "exploiting social network structure for person to person sentiment analysis"
  Abstract: Person-to-person evaluations are prevalent in all kinds of discourse and important for establishing reputations, building social bonds, and shaping public opinion. Such evaluations can be analyzed separately using signed social networks and textual sentiment analysis, but this misses the rich interactions between language and social context. To capture such interactions, we develop a model that predicts individual A's opinion of individual B by synthesizing information from the signed social network in which A and B are embedded with sentiment analysis of the evaluative texts relating A to B. We prove that this problem is NP-hard but can be relaxed to an efficiently solvable hinge-loss Markov random field, and we show that this implementation outperforms text-only and network-only versions in two very different datasets involving community-level decision-making: the Wikipedia Requests for Adminship corpus and the Convote U.S. Congressional speech corpus.

Connected through hyperedge HE-1 (induced by source paper "user profiling using hinge loss markov random fields"):
  with members:
    - "hinge loss markov random fields and probabilistic soft logic"

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"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-3 (induced by source paper "learning edge properties in graphs from path aggregations"):
  with members:
    - "link prediction based on graph neural networks"
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"
    - "effective approaches to attention based neural machine translation"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "line large scale information network embedding"
    - "adam a method for stochastic optimization"
    - "deepwalk online learning of social representations"

Connected through hyperedge HE-4 (induced by source paper "modeling drug disease relations with linguistic and knowledge graph constraints"):
  with members:
    - "hinge loss markov random fields convex inference for structured prediction"
    - "probabilistic similarity logic"

Connected through hyperedge HE-5 (induced by source paper "social network fusion and mining a survey"):
  with members:
    - "bl ecd broad learning based enterprise community detection via hierarchical structure fusion"
    - "bl mne emerging heterogeneous social network embedding through broad learning with aligned autoencoder"
    - "bicycle sharing system analysis and trip prediction"
    - "node2vec scalable feature learning for networks"
    - "organizational chart inference"
    - "a survey of signed network mining in social media"
    - "line large scale information network embedding"
    - "deepwalk online learning of social representations"

Connected through hyperedge HE-6 (induced by source paper "representing social media users for sarcasm detection"):
  with members:
    - "reasoning with sarcasm by reading in between"
    - "with 1 follower i must be awesome p exploring the role of irony markers in irony recognition"
    - "cascade contextual sarcasm detection in online discussion forums"
    - "a large self annotated corpus for sarcasm"
    - "advances in pre training distributed word representations"
    - "reporting score distributions makes a difference performance study of lstm networks for sequence tagging"
    - "a deeper look into sarcastic tweets using deep convolutional neural networks"
    - "modelling context with user embeddings for sarcasm detection in social media"

Connected through hyperedge HE-7 (induced by source paper "multiple partitioning of multiplex signed networks application to european parliament votes"):
  with members:
    - "signed graph analysis for the interpretation of voting behavior"
    - "brazilian congress structural balance analysis"
    - "relevance of negative links in graph partitioning a case study using votes from the european parliament"
    - "generalized measures for the evaluation of community detection methods"
    - "synchronization over z2 and community detection in signed multiplex networks with constraints"
    - "objective criteria for the evaluation of clustering methods"

Connected through hyperedge HE-8 (induced by source paper "compas community preserving sampling for streaming graphs"):
  with members:
    - "discriminative link prediction using local links node features and community structure"
    - "network sampling from static to streaming graphs"
    - "defining and evaluating network communities based on ground truth"
    - "influence diffusion dynamics and influence maximization in social networks with friend and foe relationships"
    - "benefits of bias towards better characterization of network sampling"
    - "estimating and sampling graphs with multidimensional random walks"

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)