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: "detecting strong ties using network motifs"
  Abstract: Detecting strong ties among users in social and information networks is a fundamental operation that can improve performance on a multitude of personalization and ranking tasks. There are a variety of ways a tie can be deemed ``strong'', and in this work we use a data-driven (or supervised) approach by assuming that we are provided a sample set of edges labeled as strong ties in the network. Such labeled edges are often readily obtained from the social network as users often participate in multiple overlapping networks via features such as following and messaging. These networks may vary greatly in size, density and the information they carry --- for instance, a heavily-used dense network (such as the network of followers) commonly overlaps with a secondary sparser network composed of strong ties (such as a network of email or phone contacts). This setting leads to a natural strong tie detection task: given a small set of labeled strong tie edges, how well can one detect unlabeled strong ties in the remainder of the network?#R##N##R##N#This task becomes particularly daunting for the Twitter network due to scant availability of pairwise relationship attribute data, and sparsity of strong tie networks such as phone contacts. Given these challenges, a natural approach is to instead use structural network features for the task, produced by combining the strong and ``weak'' edges. In this work, we demonstrate via experiments on Twitter data that using only such structural network features is sufficient for detecting strong ties with high precision. These structural network features are obtained from the presence and frequency of small network motifs on combined strong and weak ties. We observe that using motifs larger than triads alleviate sparsity problems that arise for smaller motifs, both due to increased combinatorial possibilities as well as benefiting strongly from searching beyond the ego network. Empirically, we observe that not all motifs are equally useful, and need to be carefully constructed from the combined edges in order to be effective for strong tie detection. Finally, we reinforce our experimental findings with providing theoretical justification that suggests why incorporating these larger sized motifs as features could lead to increased performance in planted graph models.

Connected through hyperedge HE-1 (induced by source paper "the power of pivoting for exact clique counting"):
  with members:
    - "higher order clustering in networks"
    - "escape efficiently counting all 5 vertex subgraphs"
    - "higher order organization of complex networks"
    - "scalable motif aware graph clustering"
    - "clique counting in mapreduce algorithms and experiments"
    - "path sampling a fast and provable method for estimating 4 vertex subgraph counts"
    - "finding the hierarchy of dense subgraphs using nucleus decompositions"
    - "wedge sampling for computing clustering coefficients and triangle counts on large graphs"

Connected through hyperedge HE-2 (induced by source paper "tie strength in online social networks and its applications a brief study"):
  with members:
    - "link classification and tie strength ranking in online social networks with exogenous interaction networks"
    - "routing in mobile ad hoc networks using social tie strengths and mobility plans"
    - "link prediction in social networks the state of the art"

Connected through hyperedge HE-3 (induced by source paper "new a generic learning model for tie strength prediction in networks"):
  with members:
    - "prediction of links and weights in networks by reliable routes"
    - "a survey of signed network mining in social media"

Connected through hyperedge HE-4 (induced by source paper "efficiently counting vertex orbits of all 5 vertex subgraphs by evoke"):
  with members:
    - "heterogeneous network motifs"
    - "higher order clustering in networks"
    - "wrs waiting room sampling for accurate triangle counting in real graph streams"
    - "escape efficiently counting all 5 vertex subgraphs"
    - "higher order organization of complex networks"
    - "a fast and provable method for estimating clique counts using tur an s theorem"
    - "scalable motif aware graph clustering"
    - "beyond triangles a distributed framework for estimating 3 profiles of large graphs"

Connected through hyperedge HE-5 (induced by source paper "subgraph networks with application to structural feature space expansion"):
  with members:
    - "adaptive graph convolutional neural networks"
    - "motifs in temporal networks"
    - "distributed representation of subgraphs"
    - "graph2vec learning distributed representations of graphs"
    - "wide deep learning for recommender systems"
    - "higher order organization of complex networks"
    - "node2vec scalable feature learning for networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"

Connected through hyperedge HE-6 (induced by source paper "ranking users in social networks with motif based pagerank"):
  with members:
    - "motifs in temporal networks"
    - "waddling random walk fast and accurate mining of motif statistics in large graphs"
    - "escape efficiently counting all 5 vertex subgraphs"
    - "higher order organization of complex networks"
    - "path sampling a fast and provable method for estimating 4 vertex subgraph counts"
    - "pagerank beyond the web"
    - "subgraph frequencies mapping the empirical and extremal geography of large graph collections"
    - "applying weighted pagerank to author citation networks"

Connected through hyperedge HE-7 (induced by source paper "motif enhanced recommendation over heterogeneous information network"):
  with members:
    - "ranking users in social networks with motif based pagerank"
    - "learning with heterogeneous side information fusion for recommender systems"
    - "billion scale commodity embedding for e commerce recommendation in alibaba"
    - "motifs in temporal networks"
    - "heterogeneous information network embedding for recommendation"
    - "higher order organization of complex networks"

Connected through hyperedge HE-8 (induced by source paper "consistent recovery threshold of hidden nearest neighbor graphs"):
  with members:
    - "hidden hamiltonian cycle recovery via linear programming"
    - "social networks that matter twitter under the microscope"

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)