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: "a deep learning approach for multimodal deception detection"
  Abstract: Automatic deception detection is an important task that has gained momentum in computational linguistics due to its potential applications. In this paper, we propose a simple yet tough to beat multi-modal neural model for deception detection. By combining features from different modalities such as video, audio, and text along with Micro-Expression features, we show that detecting deception in real life videos can be more accurate. Experimental results on a dataset of real-life deception videos show that our model outperforms existing techniques for deception detection with an accuracy of 96.14% and ROC-AUC of 0.9799.

Connected through hyperedge HE-1 (induced by source paper "emotions don t lie a deepfake detection method using audio visual affective cues"):
  with members:
    - "multi task learning for detecting and segmenting manipulated facial images and videos"
    - "recurrent convolutional strategies for face manipulation detection in videos"
    - "the deepfake detection challenge dfdc preview dataset"
    - "celeb df a new dataset for deepfake forensics"
    - "mesonet a compact facial video forgery detection network"
    - "exposing deep fakes using inconsistent head poses"
    - "exposing deepfake videos by detecting face warping artifacts"
    - "capsule forensics using capsule networks to detect forged images and videos"

Connected through hyperedge HE-2 (induced by source paper "the liar s walk detecting deception with gait and gesture"):
  with members:
    - "anomaly detection in video sequence with appearance motion correspondence"
    - "learning regularity in skeleton trajectories for anomaly detection in videos"
    - "context aware emotion recognition networks"
    - "step spatial temporal graph convolutional networks for emotion perception from gaits"
    - "gait recognition via disentangled representation learning"
    - "identifying emotions from walking using affective and deep features"
    - "pose guided human video generation"
    - "real world anomaly detection in surveillance videos"

Connected through hyperedge HE-3 (induced by source paper "are you for real detecting identity fraud via dialogue interactions"):
  with members:
    - "incremental learning from scratch for task oriented dialogue systems"
    - "relational inductive biases deep learning and graph networks"
    - "learning symmetric collaborative dialogue agents with dynamic knowledge graph embeddings"
    - "feudal networks for hierarchical reinforcement learning"
    - "composite task completion dialogue policy learning via hierarchical deep reinforcement learning"
    - "composite task completion dialogue policy learning via hierarchical deep reinforcement learning"
    - "end to end task completion neural dialogue systems"
    - "hybrid code networks practical and efficient end to end dialog control with supervised and reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "detecting deception in political debates using acoustic and textual features"):
  with members:
    - "fact checking meets fauxtography verifying claims about images"
    - "all in one multi task learning for rumour verification"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "fully automated fact checking using external sources"
    - "analysing how people orient to and spread rumours in social media by looking at conversational threads"
    - "leveraging joint interactions for credibility analysis in news communities"
    - "automatic dialect detection in arabic broadcast speech"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-5 (induced by source paper "towards automatic detection of misinformation in online medical videos"):
  with members:
    - "fake cures user centric modeling of health misinformation in social media"
    - "youtube for patient education a deep learning approach for understanding medical knowledge from user generated videos"
    - "automatic detection of fake news"
    - "fake news detection on social media a data mining perspective"

Connected through hyperedge HE-6 (induced by source paper "face focused cross stream network for deception detection in videos"):
  with members:
    - "eco efficient convolutional network for online video understanding"
    - "zero and few shot learning with semantic feature synthesis and competitive learning"
    - "comparator networks"
    - "heterogeneous knowledge transfer in video emotion recognition attribution and summarization"
    - "a simple neural network module for relational reasoning"
    - "beyond triplet loss a deep quadruplet network for person re identification"
    - "adversarial learning for neural dialogue generation"
    - "learning to compare relation network for few shot learning"

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.CL(Computation and Language)