human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "hypergraph motifs concepts algorithms and discoveries"):
  Abstract: Hypergraphs naturally represent group interactions, which are omnipresent in many domains: collaborations of researchers, co-purchases of items, joint interactions of proteins, to name a few. In this work, we propose tools for answering the following questions in a systematic manner: (Q1) what are structural design principles of real-world hypergraphs? (Q2) how can we compare local structures of hypergraphs of different sizes? (Q3) how can we identify domains which hypergraphs are from? We first define hypergraph motifs (h-motifs), which describe the connectivity patterns of three connected hyperedges. Then, we define the significance of each h-motif in a hypergraph as its occurrences relative to those in properly randomized hypergraphs. Lastly, we define the characteristic profile (CP) as the vector of the normalized significance of every h-motif. Regarding Q1, we find that h-motifs' occurrences in 11 real-world hypergraphs from 5 domains are clearly distinguished from those of randomized hypergraphs. In addition, we demonstrate that CPs capture local structural patterns unique in each domain, and thus comparing CPs of hypergraphs addresses Q2 and Q3. Our algorithmic contribution is to propose MoCHy, a family of parallel algorithms for counting h-motifs' occurrences in a hypergraph. We theoretically analyze their speed and accuracy, and we show empirically that the advanced approximate version MoCHy-A+ is up to 25X more accurate and 32X faster than the basic approximate and exact versions, respectively.

Includes members:
  Member "how much and when do we need higher order information in hypergraphs a case study on hyperedge prediction":
    also connected through other hyperedges:
      (none)
  Member "heterogeneous network motifs":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a survey on subgraph counting concepts algorithms and applications to network motifs and graphlets")
  Member "edmot an edge enhancement approach for motif aware community detection":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "an api oriented open source python framework for unsupervised learning on graphs")
  Member "hypergraph clustering with categorical edge labels":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "parameterized objectives and algorithms for clustering bipartite graphs and hypergraphs")
  Member "motivo fast motif counting via succinct color coding and adaptive sampling":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "finding temporal patterns using algebraic fingerprints")
  Member "mining frequent patterns in evolving graphs":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "finding temporal patterns using algebraic fingerprints")
  Member "rept a streaming algorithm of approximating global and local triangle counts in parallel":
    also connected through other hyperedges:
      (none)
  Member "simplicial closure and higher order link prediction":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "localized flow based clustering in hypergraphs")

Question: Please tell me which class the source paper 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)