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: "estimation of graphlet statistics"
  Abstract: Graphlets are induced subgraphs of a large network and are important for understanding and modeling complex networks. Despite their practical importance, graphlets have been severely limited to applications and domains with relatively small graphs. Most previous work has focused on exact algorithms, however, it is often too expensive to compute graphlets exactly in massive networks with billions of edges, and finding an approximate count is usually sufficient for many applications. In this work, we propose an unbiased graphlet estimation framework that is (a) fast with significant speedups compared to the state-of-the-art, (b) parallel with nearly linear-speedups, (c) accurate with <1% relative error, (d) scalable and space-efficient for massive networks with billions of edges, and (e) flexible for a variety of real-world settings, as well as estimating macro and micro-level graphlet statistics (e.g., counts) of both connected and disconnected graphlets. In addition, an adaptive approach is introduced that finds the smallest sample size required to obtain estimates within a given user-defined error bound. On 300 networks from 20 domains, we obtain <1% relative error for all graphlets. This is significantly more accurate than existing methods while using less data. Moreover, it takes a few seconds on billion edge graphs (as opposed to days/weeks). These are by far the largest graphlet computations to date.

Connected through hyperedge HE-1 (induced by source paper "a survey on subgraph counting concepts algorithms and applications to network motifs and graphlets"):
  with members:
    - "heterogeneous network motifs"
    - "variational principle for scale free network motifs"
    - "the sketching complexity of graph and hypergraph counting"
    - "motifs in temporal networks"
    - "large scale network motif learning with compression"
    - "butterfly counting in bipartite networks"
    - "combinatorial algorithm for counting small induced graphs and orbits"
    - "waddling random walk fast and accurate mining of motif statistics in large graphs"

Connected through hyperedge HE-2 (induced by source paper "local algorithms for hierarchical dense subgraph discovery"):
  with members:
    - "shared memory graph truss decomposition"
    - "static graph challenge subgraph isomorphism"
    - "higher order organization of complex networks"
    - "locally estimating core numbers"
    - "path sampling a fast and provable method for estimating 4 vertex subgraph counts"
    - "parallel peeling algorithms"
    - "graph cluster randomization network exposure to multiple universes"
    - "truss decomposition in massive networks"

Connected through hyperedge HE-3 (induced by source paper "network reconstruction and controlling based on structural regularity analysis"):
  with members:
    - "network structure from rich but noisy data"
    - "predicting drug target interactions via sparse learning"
    - "link prediction via matrix completion"
    - "higher order organization of complex networks"
    - "robust principal component analysis"

Connected through hyperedge HE-4 (induced by source paper "deep graph attention model"):
  with members:
    - "gram graph based attention model for healthcare representation learning"
    - "condensed memory networks for clinical diagnostic inferencing"
    - "end to end memory networks"
    - "deep reinforcement learning in large discrete action spaces"
    - "effective approaches to attention based neural machine translation"
    - "show attend and tell neural image caption generation with visual attention"
    - "abc cnn an attention based convolutional neural network for visual question answering"
    - "adam a method for stochastic optimization"

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)