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: "systematic topology analysis and generation using degree correlations"
  Abstract: Researchers have proposed a variety of metrics to measure important graph properties, for instance, in social, biological, and computer networks. Values for a particular graph metric may capture a graph's resilience to failure or its routing efficiency. Knowledge of appropriate metric values may influence the engineering of future topologies, repair strategies in the face of failure, and understanding of fundamental properties of existing networks. Unfortunately, there are typically no algorithms to generate graphs matching one or more proposed metrics and there is little understanding of the relationships among individual metrics or their applicability to different settings. We present a new, systematic approach for analyzing network topologies. We first introduce the dK-series of probability distributions specifying all degree correlations within d-sized subgraphs of a given graph G. Increasing values of d capture progressively more properties of G at the cost of more complex representation of the probability distribution. Using this series, we can quantitatively measure the distance between two graphs and construct random graphs that accurately reproduce virtually all metrics proposed in the literature. The nature of the dK-series implies that it will also capture any future metrics that may be proposed. Using our approach, we construct graphs for d=0, 1, 2, 3 and demonstrate that these graphs reproduce, with increasing accuracy, important properties of measured and modeled Internet topologies. We find that the d=2 case is sufficient for most practical purposes, while d=3 essentially reconstructs the Internet AS-and router-level topologies exactly. We hope that a systematic method to analyze and synthesize topologies offers a significant improvement to the set of tools available to network topology and protocol researchers.

Connected through hyperedge HE-1 (induced by source paper "recent advances in scalable network generation"):
  with members:
    - "efficiently generating geometric inhomogeneous and hyperbolic random graphs"
    - "exploring randomly wired neural networks for image recognition"
    - "parallel weighted random sampling"
    - "linear work generation of r mat graphs"
    - "egbter capturing degree distribution clustering coefficients and community structure in a single random graph model"
    - "multiscale planar graph generation"
    - "parallel and i o efficient randomisation of massive networks using global curveball trades"
    - "efficient parallel random sampling vectorized cache efficient and online"

Connected through hyperedge HE-2 (induced by source paper "graph generators state of the art and open challenges"):
  with members:
    - "a survey on theoretical advances of community detection in networks"
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "learning deep generative models of graphs"
    - "gmark schema driven generation of graphs and queries"
    - "motifs in temporal networks"
    - "g core a core for future graph query languages"
    - "darwini generating realistic large scale social graphs"

Connected through hyperedge HE-3 (induced by source paper "on the capacity of fractal d2d social networks with hierarchical communications"):
  with members:
    - "not call me cellular any more the emergence of scaling law fractal patterns and small world in wireless networks"
    - "wireless fractal cellular networks"
    - "fundamental limits on throughput capacity in information centric networks"
    - "network capacity region and minimum energy function for a delay tolerant mobile ad hoc network"
    - "evaluating mobility pattern space routing for dtns"
    - "capacity and character expansions moment generating function and other exact results for mimo correlated channels"

Connected through hyperedge HE-4 (induced by source paper "target privacy preserving for social networks"):
  with members:
    - "privacy in social media identification mitigation and applications"
    - "multi round influence maximization"
    - "contaminant removal for android malware detection systems"
    - "preserving link privacy in social network based systems"
    - "a model of internet topology using k shell decomposition"

Connected through hyperedge HE-5 (induced by source paper "security analysis methods on ethereum smart contract vulnerabilities a survey"):
  with members:
    - "easyflow keep ethereum away from overflow"
    - "a security case study for blockchain games"
    - "a hybrid formal verification system in coq for ensuring the reliability and security of ethereum based service smart contracts"
    - "finding the greedy prodigal and suicidal contracts at scale"
    - "ethir a framework for high level analysis of ethereum bytecode"
    - "formal process virtual machine for smart contracts verification"
    - "scilla a smart contract intermediate level language"
    - "empirical vulnerability analysis of automated smart contracts security testing on blockchains"

Connected through hyperedge HE-6 (induced by source paper "on the privacy of dk random graphs"):
  with members:
    - "an efficient and robust social network de anonymization attack"
    - "privacy games optimal user centric data obfuscation"
    - "prolonging the hide and seek game optimal trajectory privacy for location based services"
    - "an efficient reconciliation algorithm for social networks"
    - "graph annotations in modeling complex network topologies"
    - "de anonymizing social networks"

Connected through hyperedge HE-7 (induced by source paper "automatic discovery of families of network generative processes"):
  with members:
    - "symbolic regression of generative network models"
    - "evaluating link prediction methods"
    - "generating constrained random graphs using multiple edge switches"

Connected through hyperedge HE-8 (induced by source paper "privacy in social media identification mitigation and applications"):
  with members:
    - "social media and user privacy"
    - "similar but different exploiting users congruity for recommendation systems"
    - "a survey of location prediction on twitter"
    - "securing social media user data an adversarial approach"
    - "blind de anonymization attacks using social networks"
    - "walk2friends inferring social links from mobility profiles"
    - "on content based recommendation and user privacy in social tagging systems"
    - "identifying community structures in dynamic 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.NI(Networking and Internet Architecture)