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: "trace complexity of network inference"
  Abstract: The network inference problem consists of reconstructing the edge set of a network given traces representing the chronology of infection times as epidemics spread through the network. This problem is a paradigmatic representative of prediction tasks in machine learning that require deducing a latent structure from observed patterns of activity in a network, which often require an unrealistically large number of resources (e.g., amount of available data, or computational time). A fundamental question is to understand which properties we can predict with a reasonable degree of accuracy with the available resources, and which we cannot. We define the trace complexity as the number of distinct traces required to achieve high fidelity in reconstructing the topology of the unobserved network or, more generally, some of its properties. We give algorithms that are competitive with, while being simpler and more efficient than, existing network inference approaches. Moreover, we prove that our algorithms are nearly optimal, by proving an information-theoretic lower bound on the number of traces that an optimal inference algorithm requires for performing this task in the general case. Given these strong lower bounds, we turn our attention to special cases, such as trees and bounded-degree graphs, and to property recovery tasks, such as reconstructing the degree distribution without inferring the network. We show that these problems require a much smaller (and more realistic) number of traces, making them potentially solvable in practice.

Connected through hyperedge HE-1 (induced by source paper "learning mixtures of graphs from epidemic cascades"):
  with members:
    - "learning graphs from noisy epidemic cascades"
    - "meta learning with relational information for short sequences"
    - "em converges for a mixture of many linear regressions"
    - "the cost of uncertainty in curing epidemics"
    - "fake news mitigation via point process based intervention"
    - "list decodable robust mean estimation and learning mixtures of spherical gaussians"
    - "characterization and inference of graph diffusion processes from observations of stationary signals"
    - "solving a mixture of many random linear equations by tensor decomposition and alternating minimization"

Connected through hyperedge HE-2 (induced by source paper "learning graphs from noisy epidemic cascades"):
  with members:
    - "the cost of uncertainty in curing epidemics"
    - "fake news mitigation via point process based intervention"
    - "hiding the rumor source"
    - "correlated cascades compete or cooperate"
    - "a lower bound on the performance of dynamic curing policies for epidemics on graphs"
    - "seismic a self exciting point process model for predicting tweet popularity"
    - "spy vs spy rumor source obfuscation"
    - "an efficient curing policy for epidemics on graphs"

Connected through hyperedge HE-3 (induced by source paper "reconstructing trees from traces"):
  with members:
    - "beyond trace reconstruction population recovery from the deletion channel"
    - "trace reconstruction generalized and parameterized"
    - "optimal mean based algorithms for trace reconstruction"

Connected through hyperedge HE-4 (induced by source paper "contrastive training for models of information cascades"):
  with members:
    - "estimating diffusion network structures recovery conditions sample complexity soft thresholding algorithm"
    - "modeling information propagation with survival theory"
    - "structure and dynamics of information pathways in online media"
    - "uncovering the temporal dynamics of diffusion networks"
    - "on the convexity of latent social network inference"

Connected through hyperedge HE-5 (induced by source paper "network topology inference using information cascades with limited statistical knowledge"):
  with members:
    - "estimating infection sources in networks using partial timestamps"
    - "back to the source an online approach for sensor placement and source localization"
    - "network topology inference from spectral templates"
    - "infection spreading and source identification a hide and seek game"
    - "robust decentralized detection and social learning in tandem networks"
    - "whose opinion to follow in multihypothesis social learning a large deviations perspective"
    - "learning laplacian matrix in smooth graph signal representations"
    - "how to identify an infection source with limited observations"

Connected through hyperedge HE-6 (induced by source paper "learning network structures from contagion"):
  with members:
    - "submodular inference of diffusion networks from multiple trees"
    - "inferring networks of diffusion and influence"
    - "on the convexity of latent social network inference"

Connected through hyperedge HE-7 (induced by source paper "semi supervised learning on graphs through reach and distance diffusion"):
  with members:
    - "node2vec scalable feature learning for networks"
    - "revisiting semi supervised learning with graph embeddings"
    - "scalable facility location for massive graphs on pregel like systems"
    - "multi objective weighted sampling"
    - "reverse ranking by graph structure model and scalable algorithms"
    - "large scale distributed semi supervised learning using streaming approximation"
    - "deepwalk online learning of social representations"
    - "fast ppr scaling personalized pagerank estimation for large graphs"

Connected through hyperedge HE-8 (induced by source paper "robust influence maximization"):
  with members:
    - "robust influence maximization"
    - "online influence maximization"
    - "stability of influence maximization"
    - "estimating diffusion network structures recovery conditions sample complexity soft thresholding algorithm"
    - "combinatorial multi armed bandit and its extension to probabilistically triggered arms"
    - "analytical approach to parallel repetition"
    - "scalable influence estimation in continuous time diffusion networks"
    - "maximizing social influence in nearly optimal time"

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.DS(Data Structures and Algorithms)