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: "prep path based relevance from a probabilistic perspective in heterogeneous information networks"
  Abstract: As a powerful representation paradigm for networked and multi-typed data, the heterogeneous information network (HIN) is ubiquitous. Meanwhile, defining proper relevance measures has always been a fundamental problem and of great pragmatic importance for network mining tasks. Inspired by our probabilistic interpretation of existing path-based relevance measures, we propose to study HIN relevance from a probabilistic perspective. We also identify, from real-world data, and propose to model cross-meta-path synergy, which is a characteristic important for defining path-based HIN relevance and has not been modeled by existing methods. A generative model is established to derive a novel path-based relevance measure, which is data-driven and tailored for each HIN. We develop an inference algorithm to find the maximum a posteriori (MAP) estimate of the model parameters, which entails non-trivial tricks. Experiments on two real-world datasets demonstrate the effectiveness of the proposed model and relevance measure.

Connected through hyperedge HE-1 (induced by source paper "neural embedding propagation on heterogeneous networks"):
  with members:
    - "similarity modeling on heterogeneous networks via automatic path discovery"
    - "meta graph based hin spectral embedding methods analyses and insights"
    - "user guided clustering in heterogeneous information networks via motif based comprehensive transcription"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "meta path guided embedding for similarity search in large scale heterogeneous information networks"
    - "higher order organization of complex networks"
    - "task guided and path augmented heterogeneous network embedding for author identification"
    - "semi supervised classification with graph convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "similarity modeling on heterogeneous networks via automatic path discovery"):
  with members:
    - "aspem embedding learning by aspects in heterogeneous information networks"
    - "deeppath a reinforcement learning method for knowledge graph reasoning"
    - "go for a walk and arrive at the answer reasoning over paths in knowledge bases using reinforcement learning"
    - "learning combinatorial optimization algorithms over graphs"
    - "heterogeneous information network embedding for meta path based proximity"
    - "meta path guided embedding for similarity search in large scale heterogeneous information networks"
    - "neural combinatorial optimization with reinforcement learning"
    - "continuous control with deep reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "meta graph based hin spectral embedding methods analyses and insights"):
  with members:
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "submodular hypergraphs p laplacians cheeger inequalities and spectral clustering"
    - "aspem embedding learning by aspects in heterogeneous information networks"
    - "heterogeneous information network embedding for meta path based proximity"
    - "multi view graph embedding with hub detection for brain network analysis"
    - "meta path guided embedding for similarity search in large scale heterogeneous information networks"
    - "higher order organization of complex networks"
    - "node2vec scalable feature learning for networks"

Connected through hyperedge HE-4 (induced by source paper "automatic table completion using knowledge base"):
  with members:
    - "table to text describing table region with natural language"
    - "ad hoc table retrieval using semantic similarity"
    - "on the fly table generation"
    - "entitables smart assistance for entity focused tables"
    - "xgboost a scalable tree boosting system"
    - "adam a method for stochastic optimization"
    - "finding patterns in a knowledge base using keywords to compose table answers"
    - "convolutional neural networks for sentence classification"

Connected through hyperedge HE-5 (induced by source paper "ahine adaptive heterogeneous information network embedding"):
  with members:
    - "graph neural networks a review of methods and applications"
    - "regal representation learning based graph alignment"
    - "struc2vec learning node representations from structural identity"
    - "heterogeneous information network embedding for recommendation"
    - "bl mne emerging heterogeneous social network embedding through broad learning with aligned autoencoder"
    - "meta path guided embedding for similarity search in large scale heterogeneous information networks"
    - "xgboost a scalable tree boosting system"
    - "node2vec scalable feature learning for networks"

Connected through hyperedge HE-6 (induced by source paper "grease a generative model for relevance search over knowledge graphs"):
  with members:
    - "a comprehensive survey of graph embedding problems techniques and applications"
    - "querying knowledge graphs by example entity tuples"
    - "supervised random walks predicting and recommending links in social networks"

Connected through hyperedge HE-7 (induced by source paper "easing embedding learning by comprehensive transcription of heterogeneous information networks"):
  with members:
    - "aspem embedding learning by aspects in heterogeneous information networks"
    - "struc2vec learning node representations from structural identity"
    - "learning edge representations via low rank asymmetric projections"
    - "representation learning on graphs methods and applications"
    - "name disambiguation in anonymized graphs using network embedding"
    - "poincar e embeddings for learning hierarchical representations"
    - "meta path guided embedding for similarity search in large scale heterogeneous information networks"
    - "node2vec scalable feature learning for networks"

Connected through hyperedge HE-8 (induced by source paper "aspem embedding learning by aspects in heterogeneous information networks"):
  with members:
    - "struc2vec learning node representations from structural identity"
    - "learning edge representations via low rank asymmetric projections"
    - "bilingual learning of multi sense embeddings with discrete autoencoders"
    - "node2vec scalable feature learning for networks"
    - "linear algebraic structure of word senses with applications to polysemy"
    - "efficient non parametric estimation of multiple embeddings per word in vector space"
    - "a survey of heterogeneous information network analysis"
    - "pte predictive text embedding through large scale heterogeneous text networks"

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)